This paper presents a new fuzzy time series forecasting model based on technical analysis, affinity propagation (AP) clustering, and a support vector regression (SVR) model. Technical analysis indicators are divided into three categories to construct multivariate fuzzy logical relationships. AP clustering without specifying the number of clusters is used to obtain a suitable partition for the universe of discourse, and the representative exemplars are generated as defuzzied values. The SVR model is employed to explore the unrecognized relationships and modify the forecasts. In addition, the error-based evaluation criteria are applied to evaluate the methods. The performance of the method is evaluated using the Taiwan Capitalization Weighted Stock Index (TAIEX), Standard & Poor's 500 Index (S&P500), and Dow Jones Industrial Average (DJIA) dataset, and the experimental results demonstrate that the proposed method outperforms some classic models.

Recovery of waste electrical and electronic equipment (WEEE) plays an important role in protecting environment and conserving resources. Design of a more efficient WEEE recovery system is an imperative need for the relevant decision-makers, such as alleviation of overcapacity or insufficient recycling in many developing countries. In this paper, we optimize the WEEE recovery network which is associated with recycling prices and government subsidies by a nonlinear mixed integer programming approach. An integrated model is first proposed to formulate a design problem of WEEE recovery network, being involved with collection centers, two types of transfer stations, processing centers, incineration plants, landfill plants, secondhand product markets, and government subsides. The recycling prices and the transported quantities of WEEE (the number of batches) are endogenous variables of the model, being subject to a number of practical constraints. For solving this model, an algorithm is developed based on the branch and bound method. Scenario analysis and numerical experiments indicate that: (1) appropriate capacities of transfer stations can be provided by the proposed model for designing an environmentally and economically efficient WEEE recycling system, especially for alleviating the existing overcapacity or insufficient recycle. (2) An optimal governmental subsidy can be obtained in virtue of the proposed model and algorithm. (3) Diversity of transportation modes and permission of more than one mode in the same delivery route can greatly reduce the cost of recycling WEEE. (4) Preferred awareness of environmental protection can increase the profit and the recycled quantities, as well as reduction of the total recycling cost.

The design and optimization of antennas, electromagnetic propagation, radio frequency (RF), and channel characterization are vital to the performance of the system in the rapidly developing area of wireless communication. Antennas are electronic components that transform electrical currents into electromagnetic waves and vice versa. They are critical to the operation of any wireless network and their design and optimization are therefore of paramount importance. The goal of this research was to find out how different factors, like antenna type, electromagnetic environment, RF, and channel characteristics, affect the efficiency of a wireless network. Multipath fading of 8 dB, shadow fading of 3 dB, and path loss of 100 dB were used in the study with a microstrip antenna in a suburban propagation scenario at an RF frequency of 5 GHz. Antenna design, electromagnetic propagation scenario, radio frequency (RF), and channel characteristics were all shown to have an impact on the wireless communication system performance. The performance metrics of the wireless sensor network were almost, but not quite, those that were hoped for in a real-world setting. Several avenues for future study are suggested, and the results have important implications for the design and optimization of wireless communication systems.