Understanding User’s Switching Intention on Mobile Payment Platforms

With the development of mobile payment (m-payment) service, the competition accordingly increases among m-payment market. Users face multiple choices when adopting m-payment services. It is critical for both scholars and m-payments providers to understand what the underlying factors can influence user’s switching from one incumbent m-payment platform to another. To solve this question, we employ a push-pull-mooring (PPM) framework to build the research model. We propose that user’s dissatisfaction on incumbent m-payment provider is the main push factor for user’s switching. The attractiveness of alternative and peer influence are the pull factors influencing user’s switching. Cognitive lock-in, as the mooring factor, could influence switching intention directly. Additionally, we posit that cognitive lock-in can moderate the effects of both push and pull factors on user’s switching intention. This study will use survey methodology and structural equation modelling approach to test the hypotheses

PERFORMANCE ANALYSIS OF HIGH STEEL TUBE LATTICE SUPPORT SYSTEM IN TYPHOON AREA

Research on safety of high steel tube lattice support systems in typhoon areas is still in the preliminary stage. The purpose of this paper is to study the overall buckling and overturning stability of the high steel tube lattice support systems in typhoon area. By constructing the spatial finite element model of the high steel tube lattice support system via MIDAS Civil, the optimal design of the steel tube lattice support system is carried out through the analysis of the main influencing parameters. The stability of steel pipe lattice support system is calculated theoretically, and the optimal design of steel pipe lattice support system is studied by finite element numerical method in Typhoon area. The calculation results show that Critical buckling load coefficient increases with the increase in diameter of the steel tube when the δ/d ratio of steel pipe structure is fixed. The critical load factor of the six-limb support system is slightly larger than that of the four-limb support system. When the transverse space of the support system is from 5 m to 7 m, stability increases rapidly. The best stability of the support system is obtained when the transverse space is approximately 7 m. The diagonal brace can significantly improve the stability of the steel tube lattice falsework

EFFECT OF VEHICLE QUALITY AND SPEED ON THE IMPACT CHARACTERISTICS OF AN OVERPASS BRIDGE PIER

To study the impact of the mechanical characteristics of heavy trucks on piers under different masses and speeds, a new equivalent simplified model of heavy trucks is proposed in this paper. The reliability of the calculation model is verified by studying the pier of the G1011 Ha-Tong high-speed K302+095 separated overpass, which was subjected to impact by a heavy truck. A finite element model of a heavy truck and a pier is established using the finite element software ABAQUS, and the influence of heavy truck load and impact speed on the impact force and pier stress is analysed. Results show that the peak of impact force increases with the increase in the mass and impact speed of heavy trucks. The high-stress area of the pier is concentrated in the root and the impact position, and an inclined through-crack is formed at 45° with the pier axis. The results also reveal the influence law of the quality of heavy trucks and impact speed on the impact force and stress of the pier and provide a new theoretical basis for the anti-collision design of piers and the limitation of current specifications on the high-speed impact of heavy trucks on piers

Goal-Conditioned Predictive Coding as an Implicit Planner for Offline Reinforcement Learning

Recent work has demonstrated the effectiveness of formulating decision making as a supervised learning problem on offline-collected trajectories. However, the benefits of performing sequence modeling on trajectory data is not yet clear. In this work we investigate if sequence modeling has the capability to condense trajectories into useful representations that can contribute to policy learning. To achieve this, we adopt a two-stage framework that first summarizes trajectories with sequence modeling techniques, and then employs these representations to learn a policy along with a desired goal. This design allows many existing supervised offline RL methods to be considered as specific instances of our framework. Within this framework, we introduce Goal-Conditioned Predicitve Coding (GCPC), an approach that brings powerful trajectory representations and leads to performant policies. We conduct extensive empirical evaluations on AntMaze, FrankaKitchen and Locomotion environments, and observe that sequence modeling has a significant impact on some decision making tasks. In addition, we demonstrate that GCPC learns a goal-conditioned latent representation about the future, which serves as an "implicit planner", and enables competitive performance on all three benchmarks

Relative Status Determination for Spacecraft Relative Motion Based on Dual Quaternion

For the two-satellite formation, the relative motion and attitude determination algorithm is a key component that affects the flight quality and mission efficiency. The relative status determination algorithm is proposed based on the Extended Kalman Filter (EKF) and the system state optimal estimate linearization. Aiming at the relative motion of the spacecraft formation navigation problem, the spacecraft relative kinematics and dynamics model are derived from the dual quaternion in the algorithm. Then taking advantage of EKF technique, combining with the dual quaternion integrated dynamic models, considering the navigation algorithm using the fusion measurement by the gyroscope and star sensors, the relative status determination algorithm is designed. At last the simulation is done to verify the feasibility of the algorithm. The simulation results show that the EKF algorithm has faster convergence speed and higher accuracy

Alpha-methylnorepinephrine, a selective alpha2-adrenergic agonist for cardiac resuscitation

AbstractOBJECTIVESThe purpose of this study was to investigate the effects of a selective alpha2-adrenergic agonist, alpha-methylnorepinephrine (alphaMNE) as an alternative vasopressor agent during cardiopulmonary resuscitation (CPR).BACKGROUNDFor more than 40 years, epinephrine has been the vasopressor agent of choice for CPR. Its beta- and alpha1-adrenergic effects increase myocardial oxygen consumption, magnify global myocardial ischemia and increase the severity of postresuscitation myocardial dysfunction.METHODSVentricular fibrillation (VF) was induced in 20 Sprague-Dawley rats. After 8 min of untreated VF, mechanical ventilation and precordial compression began. AlphaMNE, epinephrine or saline placebo was injected into the right atrium 2 min after the start of precordial compression. As an additional control, one group of animals was pretreated with alpha2-receptor blocker, yohimbine, before injection of alphaMNE. Defibrillation was attempted 4 min later. Left ventricular pressure, dP/dt40, negative dP/dt and cardiac index were measured for an interval of 240 min after resuscitation.RESULTSExcept for saline placebo and yohimbine-treated animals, comparable increases in coronary perfusion pressure were observed after each drug intervention. All animals were successfully resuscitated. Left ventricular diastolic pressure, cardiac index, dP/dt40and negative dP/dt were more optimal after alphaMNE; this was associated with significantly better postresuscitation survival. Pretreatment with yohimbine abolished the beneficial effects of alphaMNE.CONCLUSIONSThe selective alpha2-adrenergic agonist, alphaMNE, was as effective as epinephrine for initial cardiac resuscitation but provided strikingly better postresuscitation myocardial function and survival