Heavy metal contents of road-deposited sediment along the urban-rural gradient around Beijing and its potential contribution to runoff pollution

Understanding the contribution of road-deposited sediment (RDS) and its washoff process is essential for controlling urban runoff pollution. Ninety-seven RDS samples were collected along the urban-suburban-rural gradient from areas of five administrative units in the Beijing metropolitan region, including central urban (UCA), urban village (UVA), central suburban county (CSA), rural town (RTA), and rural village (RVA) areas. RDS washoff was evaluated with different particle sizes using a rainfall simulator. Heavy metal elements (i.e., Cr, Cu, Ni, Pb, and Zn) were estimated in both RDS and runoff samples. The RDS mass per unit area increased in the order UCA (21 ± 24 g/m(2)) ≈ CSA (20 ± 16 g/m(2)) < RTA (59 ± 63 g/m(2)) < RVA (147 ± 112 g/m(2)) ≈ UVA (147 ± 198 g/m(2)). Compared to RDS from the other administrative units, RDS from the UCA and CSA had higher metal concentrations and higher proportions of smaller particles, whereas that from the RVA and UVA had larger quantities of metals per unit area. UCA and CSA had lower potential runoff pollution contributions per unit area. Our findings imply that controlling the first flush in the UCA and CSA, and improving existing street cleaning methods and road surface conditions in the TRA, UVA, and RVA will be appropriate strategies for controlling runoff pollution from RDS.     

Managing major chemical accidents in China: towards effective risk information

Chemical industries, from their very inception, have been controversial due to the high risks they impose on safety of human beings and the environment. Recent decades have witnessed increasing impacts of the accelerating expansion of chemical industries and chemical accidents have become a major contributor to environmental and health risks in China. This calls for the establishment of an effective chemical risk management system, which requires reliable, accurate and comprehensive data in the first place. However, the current chemical accident-related data system is highly fragmented and incomplete, as different responsible authorities adopt different data collection standards and procedures for different purposes. In building a more comprehensive, integrated and effective information system, this article: (i) reviews and assesses the existing data sources and data management, (ii) analyzes data on 976 recorded major hazardous chemical accidents in China over the last 40 years, and (iii) identifies the improvements required for developing integrated risk management in China.     

Preparation of lumen-loaded kenaf pulp with magnetite (Fe3O4)

Magnetic pulps were prepared from unbleached kenaf (hibiscus cannabinus L.) kraft pulps. Fe₃O₄ or magnetite powder was used to load into the pulp’s lumen and pit. Aluminum sulphate [Al₂(SO₄)₃] (alum) and polyethylenimine (PEI), both mainly function as retention aid were used throughout the experiment and found to be beneficial in the preparation of this magnetic pulps. The ash content method was used to determine the amount of magnetite retained in the lumen and pit. The utilization of PEI up to 2% per pulp fibres was found to be the best result on lumen loading. The deposition of magnetite powder in lumen and pit is found decrease as the addition of PEI used is more than 2% per pulp fibres. Scanning electron microscope (SEM) clearly shows the distribution of magnetite deposited in the lumen. Tensile index and folding endurance of the loaded fibre decreased slightly as the percentage of loading pigment increased.     

A brief survey on adaptive video streaming quality assessment

Quality of experience (QoE) assessment for adaptive video streaming plays a significant role in advanced network management systems. It is especially challenging in case of dynamic adaptive streaming schemes over HTTP (DASH) which has increasingly complex characteristics including additional playback issues. In this paper, we provide a brief overview of adaptive video streaming quality assessment. Upon our review of related works, we analyze and compare different variations of objective QoE assessment models with or without using machine learning techniques for adaptive video streaming. Through the performance analysis, we observe that hybrid models perform better than both quality-of-service (QoS) driven QoE approaches and signal fidelity measurement. Moreover, the machine learning-based model slightly outperforms the model without using machine learning for the same setting. In addition, we find that existing video streaming QoE assessment models still have limited performance, which makes it difficult to be applied in practical communication systems. Therefore, based on the success of deep learned feature representations for traditional video quality prediction, we also apply the off-the-shelf deep convolutional neural network (DCNN) to evaluate the perceptual quality of streaming videos, where the spatio-temporal properties of streaming videos are taken into consideration. Experiments demonstrate its superiority, which sheds light on the future development of specifically designed deep learning frameworks for adaptive video streaming quality assessment. We believe this survey can serve as a guideline for QoE assessment of adaptive video streaming.     

漓江大型底栖动物空间分布及水力驱动因子

大型底栖动物群落结构及其分布是评价河流生态健康的重要指标。基于典型水文年丰、枯两季调查,分析了漓江流域中、下游典型河段底栖动物群落结构空间分布格局、摄食类群及其与水力要素的响应关系。结果表明:①典型河段群落结构中腹足纲个体占优,优势物种为中华圆田螺和短沟蜷;丰水期底栖动物密度为829 ind/m²,枯水期为264 ind/m²;②摄食类群中刮食者占优势,且滤食者、收集者和捕食者枯水期的相对丰度高于丰水期,刮食者枯水期的相对丰度低于丰水期,摄食类群中游的相对丰度大于下游;③典型河段生物多样性呈现空间异质性,且中游明显低于下游;④流速、底部剪切力及底质粒径是影响漓江底栖生物枯水期分布的主要因素,水深和层流层厚度则是影响丰水期分布的主要因素,分选系数、层流层厚度是底栖生物多样性最主要的影响因素。研究成果可为变化水力条件下漓江水生态修复提供支持。     

Facing the defect characterization and localization challenges of bridge defects on a submicronic technology (45 nm and below)

For very deep submicron technologies, 45 nm and less, bridge defects are getting more and more complex and critical. In order to find the exact root cause, accurate defect localization, precise understanding on the nature of the defect and its impact on the fine electrical behaviour of the device are mandatory. At these ultimate technologic nodes, failure analysis techniques show a real lack of efficiency on bridge defect localization while this precise location is one of the keys to find the defect root cause that allows correct implementation of corrective actions to improve yield and reliability.To face this challenge we have built a complete set of signatures related to advance Eldo simulations, performed measurement with ultimate failure analysis tools, fully characterized a microelectronic structure in advanced technology presenting a bridge defect and established a complete link between all these data and the failure location.     

Two novel feature selection approaches for web page classification

To help the growing qualitative and quantitative demands for information from the WWW, efficient automatic Web page classifiers are urgently needed. However, a classifier applied to the WWW faces a huge-scale dimensionality problem since it must handle millions of Web pages, tens of thousands of features, and hundreds of categories. When it comes to practical implementation, reducing the dimensionality is a critically important challenge. In this paper, we propose a fuzzy ranking analysis paradigm together with a novel relevance measure, discriminating power measure (DPM), to effectively reduce the input dimensionality from tens of thousands to a few hundred with zero rejection rate and small decrease in accuracy. The two-level promotion method based on fuzzy ranking analysis is proposed to improve the behavior of each relevance measure and combine those measures to produce a better evaluation of features. Additionally, the DPM measure has low computation cost and emphasizes on both positive and negative discriminating features. Also, it emphasizes classification in parallel order, rather than classification in serial order. In our experimental results, the fuzzy ranking analysis is useful for validating the uncertain behavior of each relevance measure. Moreover, the DPM reduces input dimensionality from 10,427 to 200 with zero rejection rate and with less than 5% decline (from 84.5% to 80.4%) in the test accuracy. Furthermore, to consider the impacts on classification accuracy for the proposed DPM, the experimental results of China Time and Reuter-21578 datasets have demonstrated that the DPM provides major benefit to promote document classification accuracy rate. The results also show that the DPM indeed can reduce both redundancy and noise features to set up a better classifier.     

Hydroponic tertiary treatment

Three macrophytes are found to grow well in trenches filled with a secondary effluent of a wastewater treatment plant in the absence of any support medium: Canna, Cyperus and Paspalum. Daily concentration changes of nutrients in the wastewater as well as evapotranspiration of the macrophytes were studied batchwise. After the vegetation had covered the trenches fully, the plants removed daily, 1.2–2.6 g of total nitrogen, 0.24–0.33 g of phosphate-phosphorus and 0.3–0.9 g of sulphate-sulphur per sq m. While ammonium and phosphate concentrations always decreased in water, sulphate was found to increase due to evapotranspiration. The daily water losses due to the latter was up to 20 l·m⁻² on sunny days. It was found that for extended nutrient removal under these conditions a hydroponic tertiary treatment plant could possibly produce no effluent at all. After harvesting, the aerial parts of the plant tissue contained comparable ash contents of 0.8–0.95% on wet base. Elementary analysis showed similar N and P contents of 23–26 g/kg and 2.3–3.8 g/kg respectively and differences in some inorganic elements of up to an order of magnitude between the three plants.