New types of coplanar transmission lines, V-shaped microshield coplanar waveguide coupled lines (VMCWCLs), and coplanar waveguide V-shaped microshield coupled lines (CWVMCLs), are proposed. Analytical formulas are presented for calculating the quasistatic TEM parameters of these coupled lines by means of exact conformal mapping techniques. Numerical results are also presented to illustrate the properties of these new kinds of coplanar coupled lines.

Taxi mobility information can be considered as an important source of mobile location-based information for making marketing decisions. So, studying the behavioral patterns of taxis in a Chinese city during the holidays using the global positioning system (GPS) can yield remarkable insights into people’s holiday travel patterns, as well as the odd-even day vehicle prohibition system. This paper studies the behavioral patterns of taxis during specific holidays in terms of pick-up and drop-off locations, travel distance, mobile step length, travel direction, and radius of gyration on the basis of GPS data. Our results support the idea of a polycentric city. It is concluded from the reporting results that there are no significant changes in the distribution of pick-up and drop-off locations, travel distance, or travel direction during holidays in comparison to work days. The results suggest that human travel by taxi has a stable regularity. However, the radius of gyration of movement by most of the taxis becomes significantly larger during holidays that indicate more long-distance travels. The current study will be helpful for location-based marketing during the holidays.

Short-term load forecasting is the basis of power system operation and analysis, and is of great significance to unit composition, economic scheduling, safety verification and so on. In modern power system, the influence of meteorological factors on power system load is becoming more and more prominent. However, the traditional model fails to take into account the weather factors that affect the load change, and when the weather changes greatly, the model prediction error is large. Therefore, based on the factor analysis of five meteorological factors, a neural network prediction model combining particle group algorithm is established, and the working day load of a region is predicted, compared with the actual load, the average relative error is 1.24% and the maximum relative error is 6.50%. It shows that the model has high precision. The prediction results of the model also provide the basis for the load adjustment of the power system.