Advanced phosphorus removal for secondary effluent using a natural treatment system

Mechanisms for low concentrations phosphorus removal in secondary effluent were studied, and a process was developed using limestone filters (LF), submerged macrophyte oxidation ponds (SMOPs) and a subsurface vertical flow wetland (SVFW). Pilot scale experimental models were applied in series to investigate the advanced purification of total phosphorus (TP) in secondary effluent at the Chengjiang sewage treatment plant. With a total hydraulic residence time (HRT) of 82.52 h, the average effluent TP dropped to 0.17 mg L(-1), meeting the standard for Class III surface waters. The major functions of the LF were adsorption and forced precipitation, with a particulate phosphorus (PP) removal of 82.93% and a total dissolved phosphorus (TDP) removal of 41.07%. Oxygen-releasing submerged macrophytes in the SMOPs resulted in maximum dissolved oxygen (DO) and pH values of 11.55 mg L(-1) and 8.10, respectively. This regime provided suitable conditions for chemical precipitation of TDP, which was reduced by a further 39.29%. In the SVFW, TDP was further reduced, and the TP removal in the final effluent reached 85.08%.     

基于盈亏平衡的农村饮水安全工程运行管理经济效益分析

农村饮水安全工程供水效益差,运行管理难以维持,而工程能够长效运行的前提是收入大于正常运行成本。在对影响农村饮水安全工程收入和运行成本相关因素的变化进行分析的基础上,模拟9个情景,运用盈亏平衡分析法对调查数据进行经济评价,得出影响农村饮水安全工程运行经济效果的主要问题,提出提高农村饮水安全工程运行效率的对策和建议。     

基于水管理对象的数字流域顶层设计技术方法研究

水利信息化是实现水利现代化的基础和重要标志。以水管理对象为基础,利用现代信息技术构造数字流域,集中管理流域内的水事问题,是实现流域现代化管理的有效途径。从数字流域体系架构、水管理对象构造及全息化描述、基础平台构建、业务系统的构件化开发、数字功能流域等方面详细介绍了基于水管理对象的数字流域构造技术方法。     

Error Analysis of Chebyshev-Legendre Pseudo-spectral Method for a Class of Nonclassical Parabolic Equation

Many physical phenomena are modeled by nonclassical parabolic initial boundary value problems which involve a nonclassical term u _xxt in the governed equation. Combining with the Crank-Nicolson/leapfrog scheme in time discretization, Chebyshev-Legendre pseudo-spectral method is applied to space discretization for numerically solving the nonclassical parabolic equation. The proposed approach is based on Legendre Galerkin formulation while the Chebyshev-Gauss-Lobatto (CGL) nodes are used in the computation. By using the proposed method, the computational complexity is reduced and both accuracy and efficiency are achieved. The stability and convergence are rigorously set up. The convergence rate shows ??spectral accuracy??. Numerical experiments are presented to demonstrate the effectiveness of the method and to confirm the theoretical results.     

Data stream classification with artificial endocrine system

Due to concept drifts, maintaining an up-to-date model is a challenging task for most of the current classification approaches used in data stream mining. Both the incremental classifiers and the ensemble classifiers spend most of their time in updating their temporary models and at the same time, a big sample buffer for training a classifier is necessary for most of them. These two drawbacks constrain further application in classifying a data stream. In this paper, we present a hormone based nearest neighbor classification algorithm for data stream classification, in which the classifier is updated every time a new record arrives. The records could be seen as locations in the feature space, and each location can accommodate only one endocrine cell. The classifier consists of endocrine cells on the boundaries of different classes. Every time a new record arrives, the cell that resides in the most unfit location will move to the new arrived record. In this way, the changing boundaries between different classes are recorded by the locations where endocrine cells reside in. The main advantages of the proposed method are the saving of the sample buffer and the improving of the classification accuracy. It is very important for conditions where the hardware resources are very expensive or the main memory is limited. Experiments on synthetic and real life data sets show that the proposed algorithm is able to classify data streams with less memory space and classification error.     

Recursive “concave–convex” Fisher Linear Discriminant with applications to face,handwritten digit and terrain recognition

In classification, previous studies have shown that an eigenvalue based technique can be cast as an related SVM-type problem and that by solving this SVM-type problem, the performance can be improved significantly. In this paper, we develop a recursive “concave–convex” Fisher Linear Discriminant (DR) (RPFLD) for dimension reduction technique of high-dimensional data to extract as many meaningful features as possible, which incorporates the fundamental idea behind Fisher Linear Discriminant and casts the Fisher Linear Discriminant as a “concave–convex” programming problem based on the hinge loss. The solution of our method follows from solving the related SVM-type optimization problems iteratively, which means the proposed method, can be viewed as the combination of multiple related SVM-type problems. The special formulation of our method provides convenience for constructing sparse multi-class Fisher Linear Discriminant directly. Due to use of a recursive procedure, the number of features available from RPFLD is independent of the number of classes, meaning that in contrast to the original Fisher Linear Discriminant the number of features available from our method has no upper bound. We evaluate our algorithm on the Yale, and ORL face image databases, handwritten digit database and Terrain image dataset. Experimental results show that RPFLD outperforms other Fisher Linear Discriminant algorithms.     

Satisfying quality requirements in the design of a partition‐based,distributed stock trading system

Although quality requirements (QRs) have become a major drive in today's software development, there have been very few real‐world examples in the literature that demonstrate how to meet these requirements. This paper presents such an example. Specifically, the paper describes the design of a partition‐based distributed stock trading service system that satisfies a set of QRs related to resource utilization, performance, scalability and availability. The paper evaluates this design through detailed experiments and discusses some design alternatives and the lessons learned. Central to this design are a static load distribution strategy and a dynamic load balancing strategy. The first strategy is to achieve an initial balanced workload on the system's server cluster during the system initialization time, whereas the second strategy is to maintain this balanced workload throughout the system execution time. Together, these two strategies work in unison to ensure that the server resources are efficiently utilized; the user requests are processed with the required speed; the application is partitioned with sufficient room to scale; and the system is highly available. Copyright © 2011 John Wiley & Sons, Ltd.     

Analytical solutions for sketch-based convolution surface modeling on the GPU

Convolution surfaces are attractive for modeling objects of complex evolving topology. This paper presents some novel analytical convolution solutions for planar polygon skeletons with both finite-support and infinite-support kernel functions. We convert the double integral over a planar polygon into a simple integral along the contour of the polygon based on Green’s theorem, which reduces the computational cost and allows for efficient parallel computation on the GPU. For finite support kernel functions, a skeleton clipping algorithm is presented to compute the valid skeletons. The analytical solutions are integrated into a prototype modeling system on the GPU (Graphics Processing Unit). Our modeling system supports point, polyline and planar polygon skeletons. Complex objects with arbitrary genus can be modeled easily in an interactive way. Resulting convolution surfaces with high quality are rendered with interactive ray casting.     

Performance analysis and optimization of MPI collective operations on multi-core clusters

Memory hierarchy on multi-core clusters has twofold characteristics: vertical memory hierarchy and horizontal memory hierarchy. This paper proposes new parallel computation model to unitedly abstract memory hierarchy on multi-core clusters in vertical and horizontal levels. Experimental results show that new model can predict communication costs for message passing on multi-core clusters more accurately than previous models, only incorporated vertical memory hierarchy. The new model provides the theoretical underpinning for the optimal design of MPI collective operations. Aimed at horizontal memory hierarchy, our methodology for optimizing collective operations on multi-core clusters focuses on hierarchical virtual topology and cache-aware intra-node communication, incorporated into existing collective algorithms in MPICH2. As a case study, multi-core aware broadcast algorithm has been implemented and evaluated. The results of performance evaluation show that the above methodology for optimizing collective operations on multi-core clusters is efficient.