Scholars have long recognized that product presentation plays an important role for consumers to understand product and make further decisions, but it is not well-tested in the cross-border e-commerce context. Additionally, there exist inconsistent insights regarding the influences of the two major product presentation modes currently used by cross-border e-commerce platforms (i.e., static pictures and live streaming). Therefore, drawing on regulatory focus theory, this study develops a research model to explain and differentiate how live streaming and static pictures affect cross-border purchase intentions through the motivation systems associated with different desired end states (i.e., accessing benefit and avoiding uncertainty). Partial least squares approach and relative importance analysis were used to test the model with 272 survey participants. The empirical results show that both product presentation modes can enhance cross-border purchase intentions through regulatory focus. More interestingly, it shows that static pictures are better at reducing perceived uncertainty, whereas live streaming is more effective at increasing perceived desirability, in leading to higher cross-border purchase intentions. This study contributes to differentiating the impacts of different product presentation modes in attracting cross-border purchase intentions from a regulatory focus perspective and provides practical implications for cross-border e-commerce providers and sellers to leverage distinct product presentation modes to attract consumers.

This paper addresses the optimal output regulation problem for discrete-time linear systems with completely unknown dynamics and unmeasurable exosystem states. The primary objective is to design incremental dataset-based value iteration (VI) reinforcement learning algorithms to derive both state feedback and output feedback controllers. In the context of data-driven optimal control , existing approaches typically require either the exosystem state to be measurable or the design of an autonomous system to reconstruct it. In contrast, this work proposes an incremental dataset-based VI algorithm, which eliminates the need for exosystem state measurement or reconstruction. Additionally, the proposed method allows for the selection of an arbitrary initial admissible control policy, thereby overcoming the challenge of requiring an initial admissible control in policy iteration algorithms. Furthermore, the system state is reconstructed using the incremental dataset, and an optimal output feedback controller is developed based on the proposed VI algorithm. The theoretical convergence of the dataset-based incremental VI algorithm is rigorously analyzed, and comprehensive simulations are conducted to validate its effectiveness.

Aerial geomagnetic measurement has high strategic significance and application value in geological exploration, unexploded ordnance (UXO) detection, geomagnetic navigation, etc. The magnetic interference of aircraft carrier structure materials and electronic equipment inside the cabin seriously reduces the accuracy of geomagnetic survey. The key to carrier magnetic interference compensation is to realize high-precision estimation of the interference model parameters. However, the estimation accuracy has been limited by the strong interference noises which are not described in the interference model. At the same time, the carrier magnetic interference is dynamically changing. To solve the above problems, a real-time carrier magnetic interference compensation method based on constrained Rayleigh quotient recursive total least squares (RTLSs) is proposed in this article. By considering the input and output errors in the carrier magnetic interference, we established an enhanced interference model, and designed a constrained function on basis of Rayleigh quotient (c-RQ) to acquire an unbiased adaptive solution of the compensation parameters estimation. Using this method, the parameters of carrier magnetic interference compensation model can be calculated in real time based on the prior compensation model parameters obtained by maneuvering calibration flight and the currently updated acquisition data during mission flight. To evaluate the performance of this method, simulation and experimental verification were carried out. Simulation and experimental results show that this method can effectively realize high-precision compensation of carrier magnetic interference compared with traditional methods and machine learning methods. In addition, RTLS has more efficient computing power than traditional methods and machine learning methods.

Aeromagnetic compensation is important in aeromagnetic detection. Traditionally, a calibration flight would be implemented to estimate the interference model. However, the magnetic interference is not constant during the mission flight, which makes the interference model constructed in the calibration flight inapplicable in the mission. To solve that problem, this paper proposes an evolutionary aeromagnetic compensation method based on Woodbury equation. With this method, the magnetic interference model parameters can be evolved during the mission flight on basis of the previous interference model and the updated acquired data. To evaluate the performance of the evolutionary method, both simulation and the experiment were conducted. The results indicate that the proposed method can effectively reduce the influence of changing magnetic interference. In the experiment, the standard deviation (STD) of measured data before compensation in the last mission flight is 1.2070nT, the traditional compensation can reduce the STD to 0.0863nT, while our evolutionary aeromagnetic compensation method can reduce STD to 0.0450nT. The results show that increasing changes of magnetic interference will make evolutionary aeromagnetic compensation vital for isolating signals created by geologic features from signals created by the aircraft.

Traditional geomagnetic vector matching methods are mainly based on a certain correlation criterion to filter the optimal track, that the optimal track selection function is single and unable to distinguish the nonlinear mapping of the geomagnetic field and geographical position. Since the geomagnetic matching process is similar to pattern recognition, a vector pattern recognition matching method based on a probabilistic neural network (PNN) is proposed to realize geomagnetic navigation. The neural network input is geomagnetic vector elements, and the genetic algorithm is used to optimize the PNN’s smooth parameter to classify better. The comparison of VICCP, VMAGCOM and the proposed method is carried out in simulation in two kinds of areas with significant and insignificant geomagnetic features. Simulation results show that the proposed method has the highest matching rates of 94% and 100% in two kinds of regions. The matching accuracy is also significantly better than traditional algorithms. The experiment is carried out to verify the effectiveness and robustness of the proposed method at last.

Despite the promise of cross-border e-commerce, attracting consumers is still a worldwide challenge. Many cross-border e-commerce platforms have responded to the challenges by embracing innovative tools like live streaming. However, there has been limited understandings of the unique nature of live streaming and its empirical influence. Taking an affordance view of live streaming, this study defines affordance of live streaming as the capacities provided by live streaming and examines how affordance of live streaming affect consumer behavior in the cross-border e-commerce context based on information transparency perspective. Results show that although live streaming does not directly affect consumers’ cross-border purchase intention, it can increase consumers’ purchase intention through increasing perceived information transparency. In addition, affordance of live streaming can further moderate the relationship between different types of information transparency and consumers’ cross-border purchase intention. The findings provide a much-needed contribution to academia and business.

During the geomagnetic vector navigation, there are three attitude angles required for coordinate transformation in the process of geomagnetic vector elements’ calculation. However, the yaw angle provided by inertial navigation system (INS) has accumulated error which is inevitable and will seriously reduce navigation accuracy. An improved geomagnetic navigation method based on two-component gradient weighting (TCGW) is proposed in this paper. The horizontal and vertical geomagnetic components can be calculated with roll angle and pitch angle based on the implicit trigonometric theorem in the process of coordinate transformation. And the gradient weights are used as the coefficients to improve the accuracy and universality. The simulations based on four typical types have been performed, and the Monte Carlo simulation results indicated that the proposed method has higher matching accuracy than traditional vector ICCP (VICCP) method. Furthermore, the airborne navigation experiments have been carried out to verify the accuracy and effectiveness of the proposed method.

Magnetic anomaly detection (MAD) is widely applied in the fields of resource exploration, hidden target detection, and explosive ordnance disposal. Traditional methods, such as orthonormal basis functions (OBFs), are proposed to extract anomaly signals from ambient noises and device noises. Due to the weakness of the signal, the detection probability has always been limited by a low signal-to-noise ratio (SNR). To surmount the limitation, a full connected neural network (FCN) with OBF features is trained to do the detection. Nonetheless, its effect is not reliable enough under a low SNR, and it is sensitive to the orientations. This letter introduces a multifeature fusion-based neural network with three subclassifiers to conduct MAD. The first subclassifier uses the time–frequency feature, the second uses the statistical feature, and the last concentrates on the magnetic moment feature. The outputs of the subclassifiers are analyzed synthetically by weighted voting, and the optimized weights are picked on the basis of individual performance. The real noise is recorded by experiments to test the performance of our network. The result indicates that a multifeature-based neural network shows a higher detection probability than the ordinary FCN by 5% medially. At very low SNR, the multifeature-based neural network can achieve a detection probability 13% higher than FCN. Sensitivity to orientations is also improved by the multifeature-based neural network.

Total precipitable water (TPW) values derived from Constellation Observing System for Meteorology, Ionosphere and Climate-2 (COSMIC-2) are compared with those derived from Special Sensor Microwave Imager Sounder (SSMIS), Global Precipitation Measurement (GPM) Microwave Imager (GMI), and Advanced Microwave Scanning Radiometer-2 (AMSR-2) over the ocean from October 1, 2019 to February 16, 2020. The overall comparison results indicate that TPW values derived from SSMIS, AMSR-2, and GMI have a good correlation and agreement with COSMIC-2 TPW values with the correlation coefficients greater than 0.99 and root mean square (rms) no greater than 2.7 mm. We compare TPW derived from three different microwave radiometers with COSMIC-2 TPW over the subtropical and tropical oceans. The differences illustrate that TPW values derived from three different microwave radiometers are more consistent with COSMIC-2 TPW values over the subtropical ocean than those over the tropical ocean. In addition, we also analyze the relationship between the TPW retrieval accuracy derived from three different microwave radiometers and environmental factors, including cloud, rain rate, wind speed, and surface temperature. The results indicate that four environmental factors have an important influence on the TPW retrieval from three different microwave radiometers.

Potential-field downward continuation is a crucial tool to process gravity and magnetic data, which is capable of effectively enhancing weak anomalies and identifying overlapped ones. However, available methods in this procedure do not include analysis about the impact from different frequencies of the observed magnetic data on the continuation effect. Besides, these methods contain quite a few iteration processes that fix the filter operator and result in their poor performance in reality. In this article, a new downward continuation method is presented, which is based on adaptive filtering within the iteration framework. It finds out the wavenumber distribution of observed data by analyzing its power spectrum, followed by adaptive adjustments on the passband of the filter operator, so as to effectively enhance the convergence speed and continuation accuracy. The simulation results indicated that this method could achieve adaptive adjustments as desired, and it could produce results with higher accuracy and faster convergence rate than the Landweber iterative method can. In addition, tests with actual data revealed a fast and stable downward continuation effect with the proposed method.