The Factors Affecting Cross-border E-commerce Development of SMEs ---An Empirical Study

Recently, there are two mainly methods for SMEs operating cross-border e-commerce in China. One is online store of firms via the cross-border third party platform. The other is the online store of firms built by themselves for export markets expansion. Based on the analysis of cross-border e-commerce process, we explore four factors which may affect the mode selection of cross-border e-commerce of SMEs, namely E-marketing, electronic payment, electronic customs and international logistics. For the methodology, we use Probit model of Logit modeling and then have the finding that the three factors E-marketing, electronic customs and international logistics are the impact of SMEs cross-border e-commerce mode selection, and further find that most of SMEs who are weak at cross-border e-commerce operation stay on third party cross-border e-commerce service platform. Based on the finding, we also have some conclusion. In order to assist SMEs development, the government should establish and improve the trade informational platform. Meanwhile, they should encourage the cross-border e-commerce third-party platform to become bigger and stronger and improve electronic customs clearance continuously to raise large scale cross-border logistic firms and third parties with good service, strong competition and high technologies. For the SMEs, they should adopt the third party cross-border e-commerce platform actively. Some firms should have their own cross-border websites for opening cross-border e-commerce channel actively

Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection

Accurate and fast identification of vibration signals detected based on the phase-sensitive optical time-domain reflectometer (Φ-OTDR) is crucial in reducing the false-alarm rate of the long-distance distributed vibration warning system. This study proposes a computer vision-based Φ-OTDR multi-vibration events detection method in real-time, which can effectively detect perimeter intrusion events and reduce personnel patrol costs. Pulse accumulation, pulse cancellers, median filter, and pseudo-color processing are employed for vibration signal feature enhancement to generate vibration spatio-temporal images and form a customized dataset. This dataset is used to train and evaluate an improved YOLO-A30 based on the YOLO target detection meta-architecture to improve system performance. Experiments show that using this method to process 8069 vibration data images generated from 5 abnormal vibration activities for two types of fiber optic laying scenarios, buried underground or hung on razor barbed wire at the perimeter of high-speed rail, the system [email protected] is 99.5%, 555 frames per second (FPS), and can detect a theoretical maximum distance of 135.1 km per second. It can quickly and effectively identify abnormal vibration activities, reduce the false-alarm rate of the system for long-distance multi-vibration along high-speed rail lines, and significantly reduce the computational cost while maintaining accuracy

Using Phase-Sensitive Optical Time Domain Reflectometers to Develop an Alignment-Free End-to-End Multitarget Recognition Model

This pattern recognition method can effectively identify vibration signals collected by a phase-sensitive optical time-domain reflectometer (Φ-OTDR) and improve the accuracy of alarms. An alignment-free end-to-end multi-vibration event detection method based on Φ-OTDR is proposed, effectively detecting different vibration events in different frequency bands. The pulse accumulation and pulse cancellers determine the location of vibration events. The local differential detection method demodulates the vibration event time-domain variation signals. After the extraction of the signal time-frequency features by sliding window, the convolution neural network (CNN) further extracts the signal features. It analyzes the temporal relationship of each group of signal features using a bidirectional long short-term memory network (Bi-LSTM). Finally, the connectionist temporal classification (CTC) is used to label the unsegmented sequence data to achieve single detection of multiple vibration targets. Experiments show that using this method to process the collected 8563 data, containing 5 different frequency bands of multi-vibration acoustic sensing signal, the system F1 score is 99.49% with a single detection time of 2.2 ms. The highest frequency response is 1 kHz. It is available to quickly and efficiently identify multiple vibration signals when a single demodulated acoustic sensing signal contains multiple vibration events

Improved Cycle Stability of LiSn Alloy Anode for Different Electrolyte Systems in Lithium Battery

Lithium metal anode still confronts a series of problems at the way to commercialization though it has advantages in high energy density. The formation of Li dendrite is the major limitation need to be conquered. Here, a facile and simple LiSn alloy anode prepared by a direct metallurgy method is fabricated and evaluated in both liquid electrolyte and solid electrolyte. Structural analysis and electrochemical measurements reveal the promoted ionic transference of interface and enhanced cycling stability in different electrolyte systems, without dendrite formation. Furthermore, the application of this simple and sustainable LiSn alloy can be extended to more alloy anode and might unlock the next-generation anode in the future

Time-Stamp Attacks on Remote State Estimation in Cyber-Physical Systems

In this paper, we consider the security of remote state estimation in cyber-physical systems, where a wireless sensor sends the measurement of the considered system to a remote state estimator using a wireless channel. In this scenario, time-stamp technology is commonly used to record the time characteristic of the data for different objectives, such as realizing time synchronization and dealing with time delays. We consider a potential threat to this mechanism, and investigate a time-stamp attack, where a malicious adversary manipulates data packets to degrade the estimation performance of the remote estimator. In particular, we assume that the attacker can alter the time-stamps of any two packets. We consider two types of sensor transmission, namely innovation and local estimate, respectively, and analyze the evolution of the remote estimation error covariance for both cases. Furthermore, we characterize the optimal attack strategy that maximizes the estimation error covariance from the attacker's point of view. Finally, we present numerical simulations to demonstrate the effectiveness of our results