Error-correction factor models for high-dimensional cointegrated time series

Cointegration inferences often rely on a correct specification for the short-run dynamic vector autoregression. However, this specification is unknown, a priori. A lag length that is too small leads to an erroneous inference as a result of the misspecification. In contrast, using too many lags leads to a dramatic increase in the number of parameters, especially when the dimension of the time series is high. In this paper, we develop a new methodology which adds an error-correction term for the long-run equilibrium to a latent factor model in order to model the short-run dynamic relationship. The inferences use the eigenanalysis-based methods to estimate the cointegration and latent factor process. The proposed error-correction factor model does not require an explicit specification of the short-run dynamics, and is particularly effective for high-dimensional cases, in which the standard error-correction suffers from overparametrization. In addition, the model improves the predictive performance of the pure factor model. The asymptotic properties of the proposed methods are established when the dimension of the time series is either fixed or diverging slowly as the length of the time series goes to infinity. Lastly, the performance of the model is evaluated using both simulated and real data sets

Optomechanical magnetometry with a macroscopic resonator

We demonstrate a centimeter-scale optomechanical magnetometer based on a crystalline whispering gallery mode resonator. The large size of the resonator allows high magnetic field sensitivity to be achieved in the hertz to kilohertz frequency range. A peak sensitivity of 131 pT per root Hz is reported, in a magnetically unshielded non-cryogenic environment and using optical power levels beneath 100 microWatt. Femtotesla range sensitivity may be possible in future devices with further optimization of laser noise and the physical structure of the resonator, allowing applications in high-performance magnetometry

Constrained shuffled complex evolution algorithm and its application in the automatic calibration of Xinanjiang model

The Shuffled Complex Evolution—University of Arizona (SCE-UA) is a classical algorithm in the field of hydrology and water resources, but it cannot solve constrained optimization problems directly. Using penalty functions has been the preferred method to handle constraints, but the appropriate selection of penalty parameters and penalty functions can be challenging. To enhance the universality of the SCE-UA, we propose the Constrained Shuffled Complex Evolution Algorithm (CSCE) to conveniently and effectively solve inequality-constrained optimization problems. Its performance is compared with the SCE-UA using the adaptive penalty function (SCEA) on 14 test problems with inequality constraints. It is further compared with seven other algorithms on two test problems with low success rates. To demonstrate its effect in hydrologic model calibration, the CSCE is applied to the parameter optimization of the Xinanjiang (XAJ) model under synthetic data and observed data. The results indicate that the CSCE is more advantageous than the SCEA in terms of the success rate, stability, feasible rate, and convergence speed. It can guarantee the feasibility of the solution and avoid the problem of deep soil tension water capacity (WDM)<0 in the optimization process of the XAJ model. In the case of synthetic data, the CSCE can accurately find the theoretical optimal parameters of the XAJ model under the given constraints. In the case of observed data, the XAJ model optimized by the CSCE can effectively simulate the hourly rainfall-runoff events of the Hexi Basin and achieves mean Nash efficiency coefficients greater than 0.75 in the calibration period and the validation period

Optic-Fiber Temperature Sensor

As an important parameter in industry, agriculture, biomedical, and aerospace, temperature possesses a significant position for the development of our society. Thus, it has become a hot point to develop novel sensors for temperature monitoring. Compared with traditional electronic sensors, optical fiber sensors break out for the compact structure, corrosion resistance, multiplex and remote sensing capability, cheap prices, and large transmission capacity. Especially the phase modulation type optical fiber sensors attract much attention for the fast and accurate measurement of the external parameters in a large dynamic measurement range. In this work, we review the optical fiber Mach-Zehnder interferometer (MZI) for temperature sensing which is widely used these years. The fundamental principles of MZI fiber sensors are proposed and discussed to further understand MZI. Different kind of structures for temperature sensing of recent years are summarized as several typical MZI categories and their advantages and disadvantages are indicated separately. Finally, we make a conclusion of the MZI temperature sensing and several methods typically to realize the MZI in practical application for the readers