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This paper searches for a deployment method with minimum sensor nodes to achieve agricultural intelligent monitoring. Because of the various features, topographies and complicated weather conditions, the selection of the location for deploying sensor nodes is difficult, especially in hilly areas. This paper puts forward a sensor node deployment algorithm for agricultural intelligent monitoring based on an optimized theory. It first develops a mathematical model for the node location selection. During the solution process, the features of the locations are of primary concern, as a location with more features is easier to choose than other locations. Next, a theoretical analysis and experimental simulation are carried out. Finally, OASNDFA is used in 3000 mu orange orchards in China. The result demonstrates that OASNDFA could perform relevant monitoring tasks with fewer monitoring nodes. [ABSTRACT FROM AUTHOR]

Abstract: With the start up of West–East Natural Gas Transmission Project, the construction of natural gas-pipeline will enter on a new era in China. The development tendency will be towards large-diameter, high-pressure and long-distance for natural gas-pipelines. Correspondingly, the life cycle cost of natural gas-pipeline networks is increasing gradually. The mainline system is a vital part of natural gas network systems. The investment required for the mainline system is enormous, usually accounting for 80% of the total investment for this system. In general, the investment required for a gas-pipeline depends on its operating parameters. Therefore, based on the characteristics of gas networks, optimization for investment becomes indispensable to gas networks design. A comprehensive and optimal mathematic model of a gas networks system is established in this paper which considers all the factors influencing the total investment of a gas networks system (e.g. pipe diameter, thickness, pressure, length, compression ratio, etc). From the standpoint of the characteristics of a model comprising both continuous and discrete variables, a new methodology, rank-optimization, is presented. On the basis of this model, a simple and visual high-pressure networks optimization program has been compiled. Furthermore, the developed optimization program has been applied to a practical project and the effects of operating parameters on the total investment have been analyzed. The simulation model in this paper is shown to be an effective method to solve optimization on mainline system in high-pressure gas networks. [Copyright &y& Elsevier]

In this paper, the premature failure of the integral droppers in high speed railway catenary in China was investigated. Series of microscopic characterization and analysis were conducted to identify the root causes of the unexpected failure. By numerically simulating the existing crimping behavior, distribution of the contact stress in the dropper was obtained. It was found that there was stress concentration at the dropper sliced end, which became the primary reason for the permanent distortion and damage on the droppers, and consequently resulted in the untimely final fracture with bending fretting fatigue acting. Through the failure analysis and numerical simulation, a scheme of the improvements in measuring the existing crimping was brought up. Meanwhile, a plan was also put forward to change the current crimping model and reduce the depth of indentation of clamp tube. Static tensile tests and bending fretting fatigue tests were carried out to evaluate the optional plans, which would help extend the service life of integral dropper in practice. [ABSTRACT FROM AUTHOR]

With rapid development of financial services and products, credit risk assessment has recently gained considerable attention in the field of financial risk management. In this paper, an improved credit risk assessment approach is presented. Based on the credit data from China Banking Regulatory Commission (CBRC), a multi-dimensional and multi-level credit risk indicator system is constructed. In particular, we present an improved sequential minimal optimization (SMO) learning algorithm, named four-variable SMO (FV-SMO), for credit risk classification model. At each iteration, it jointly selects four variables into the working set and an theorem is proposed to guarantee the analytical solution of sub-problem. The assessment is made on China credit dataset and two benchmark credit datasets from UCI database and CD-ROM database. Experimental results demonstrate FV-SMO is competitive in saving the computational cost and outperforms other five state-of-the-art classification methods in credit risk assessment accuracy. [ABSTRACT FROM AUTHOR]

Abstract: In China, the energy pricing mechanism has an insufficient linkage with other energy prices. As a result of the unreasonable price level, it is impossible to exploit fully the substitution elasticity among energy resources and there is a negative impact on achieving energy conservation and energy efficiency. This paper proposes an optimized mechanism for residential energy prices in China, which maximizes the total social surplus subject to some related constraints. Three types of energy pricing mechanisms are designed based on China's low-carbon targets and the optimization of residential energy price policies through the dynamic CGE model. Compared with the energy price linkage method, the results show that the market netback value mechanism has a greater impact on the total social surplus. In order to achieve further low-carbon targets, the proportion of second and third tier residents can be expanded, while the energy prices could be deregulated to some degree. In addition, considering residential affordability, the government may take into account different electricity pricing mechanisms for different tiers of residents. Electricity pricing for the first tier, the second tier and the third tier should be based respectively on cost, the integration of energy price linkage and the market netback value mechanism. [Copyright &y& Elsevier]

Abstract: In response to climate change, China''s power industry is undertaking the task of reducing carbon emissions. Renewable energy generation has become an important option. For the government and state grid companies, it is important to know the maximum possible capacities of renewable energy generation from its different sources in order to plan the construction of the power grid in the future. In this paper, several important factors affecting the development of renewable energy generation are identified through a review of the existing literature (such as cost, technical maturity and so on) and analyzed. Combined with the learning curve model, the technology diffusion model and expectations about future economic development in China, a new model, the Renewable Energy Optimization Model (REOM), is developed to analyze the development of three renewable energy sources (wind power, solar power and biomass power) from 2009 to 2020. Results show that (1) the maximum installed capacities of wind power, solar power and biomass power will reach 233321, 26680 and 35506MW in 2020; (2) from 2009 to 2020, biomass power will develop rapidly at the early stage while wind power is developed massively at the final stage and solar power has relatively stable growth; (3) due to the added capacity in the early periods, the unit investment cost of solar power shows a large decline, which is good for its following scale development; (4) the investment ratio constraint has a large effect on the development of wind power while the constraint of on-grid proportion of renewable energy generation has a significant effect on the development of wind power and solar power. The results have important policy implications for long-term energy planning in developing countries, such as China and India. [Copyright &y& Elsevier]

Abstract: Transit network design is an important part of urban transportation planning. The purpose of this paper is to build on direct traveler density model and extend it to design transit network considering demand density relating to direct demands and transfers, and lengths of routes. The proposed method aiming to maximize demand density of route under some resource constraints divides transit network design problem into three stages, i.e., skeleton route design, main route design and branch route design, based on the objective functions with different transfer coefficients. An ant colony optimization (ACO) is used to solve the model. The model and algorithm are illustrated with data from Dalian city, China and results show that the approach can improve the solution quality if the transfer coefficient is reasonably set. [Copyright &y& Elsevier]

Abstract: The benefits of Integrated Water Resources Management (IWRM) are widely known but its recommendations remain thinly implemented. Designing wastewater treatment plants for reuse in irrigation is a particularly underutilized IWRM opportunity that could potentially increase agricultural yields, conserve surface water, offset chemical fertilizer demand, and reduce the costs of wastewater treatment by eliminating nutrient removal processes. This paper presents a novel planning model, consisting of a reuse-centric performance assessment and optimization model to help design wastewater treatment plants for reuse in agriculture. The performance assessment and optimization model are described, and their coupled application is demonstrated in the peri-urban district of Pixian, China. Based on the results of the performance assessment, two reuse scenarios are evaluated: wastewater to supplement business as usual (BAU) irrigation, and wastewater to replace BAU irrigation. The results indicate that wastewater supplementation could increase profits by $20 million (M) annually; alternatively, wastewater replacement could conserve 35Mm3 of water in local rivers each year. [Copyright &y& Elsevier]

Summary: In this paper, the Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) were used to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this combined method, several SWAT models with different structures are first selected; next GA is used to calibrate each model using observed streamflow data; finally, BMA is applied to combine the ensemble predictions and provide uncertainty interval estimation. This method was tested in two contrasting basins, the Little River Experimental Basin in Georgia, USA, and the Yellow River Headwater Basin in China. The results obtained in the two case studies show that this combined method can provide deterministic predictions better than or comparable to the best calibrated model using GA. The 66.7% and 90% uncertainty intervals estimated by this method were analyzed. The differences between the percentage of coverage of observations and the corresponding expected coverage percentage are within 10% for both calibration and validation periods in these two test basins. This combined methodology provides a practical and flexible tool to attain reliable deterministic simulation and uncertainty analysis of SWAT. [Copyright &y& Elsevier]