Despite spiking neural networks (SNNs) inherently exceling at processing time series due to their rich spatio-temporal information and efficient event-driven computing, the challenge of extracting complex correlations between variables in multivariate time series (MTS) remains to be addressed. This paper proposes a reinforced, event-driven, and attention-based convolution SNN model (REAT-CSNN) with three novel features. First, a joint Gramian Angular Field and Rate (GAFR) coding scheme is proposed to convert MTS into spike images, preserving the inherent features in MTS, such as the temporal patterns and spatio-temporal correlations between time series. Second, an advanced LIF-pooling strategy is developed, which is then theoretically and empirically proved to be effective in preserving more features from the regions of interest in spike images than average-pooling strategies. Third, a convolutional block attention mechanism (CBAM) is redesigned to support spike-based input, enhancing event-driven characteristics in weighting operations while maintaining outstanding capability to capture the information encoded in spike images. Experiments on multiple MTS data sets, such as stocks and PM2.5 data sets, demonstrate that our model rivals, and even surpasses, some CNN- and RNN-based techniques, with up to 3% better performance, while consuming significantly less energy.

Despite considerable research efforts being devoted to the taxi cruising route recommendation (TCRR) problem, existing studies still have some shortcomings. To begin with, the competition and collaboration between taxis are not sufficiently taken into account. Furthermore, the TCRR is heavily reliant on potential taxi demand, which is time-variant and difficult to accurately predict due to the underlying spatiotemporal correlation and dynamic traffic patterns. Moreover, the consideration of competition and cooperation among taxis increases the complexity of the TCRR problem, making conventional centralized algorithms computationally expensive. In this paper, we first formulate TCRR as a biobjective optimization problem to balance the collaboration and competition between taxis. Subsequently, we forecast short-term taxi demand using the proposed long-short-term-memory-based graph convolutional network (LSTM-GCN), which considers diverse factors such as road topology, points of interest (POIs), and multiple time-scale features. Lastly, we propose a distributed algorithm based on a Lagrange dual decomposition. The experimental and simulation results demonstrate that our TCRR scheme performs better than any other counterpart, (i) resulting in a $3\%$ reduction in idle taxis per hour, (ii) performing four times faster than the centralized algorithms to obtain the optimal solution, and (iii) resulting in a $7\%$ increase in average profit.

The accurate calibration of the beam energy of the circular electron-positron collider (CEPC) is performed to accurately measure the mass width of Higgs particle and the mass of W/Z boson, thus providing the basic experimental basis for the accurate test of the standard model. Based on this, the error control of beam energy is required to be at a level of 10–5. Compton backscattering method is suitable for high precision calibration of beam energy in the Hundred GeV high energy electron collider. In this work, the CEPC beam energy is predicted to reach a theoretical accuracy of about 3 MeV by using the accurate measurement of the scattered photon energy after microwave electron Compton backscattering. Firstly, TM01 mode microwave transmission in circular waveguide is selected according to the design requirements, and the electromagnetic field distribution and Poynting vector under this condition are solved. According to the photon distribution and transmission in the waveguide, the design idea is proposed to simplify the complexity of calculation, and the parameters conforming to the design requirements are solved by combining the simultaneous equations of the high purity germanium detector sensitivity and the background of synchrotron radiation. Using the optimal set of waveguide inner diameter, microwave wavelength and electron incident angle data, the derivative of the differential scattering cross section with respect to energy and the collision brightness are obtained when the microwave power is 100 W. The scattered photon density of 15 MeV energy is further obtained, and the signal-to-noise ratio is analyzed according to the photon density of synchrotron radiation under this energy. The feasibility of the scheme is demonstrated theoretically and the technical difficulties and problems to be further studied are discussed.