A Study of User Adoption Factors of Mobile Banking Services Based on the Trust and Distrust Perspective

A large numbers of banks have paid attention to promote the Mobile banking service because this could provide real-time services ubiquitously and reduce the operating cost. However, the existence of risk and uncertainty in mobile banking may cause lack of trust so that consumers have pause and ponder to this services. Firstly, according to the differences between trust and distrust relationship, this article sets sight that the users’ adoption to mobile banking is decided by the trust and distrust. Distrust is mainly affected by uncertainty avoidance, perceived cost, perceived risk; in the meanwhile, trust is influenced by the trust propensity, consumer cognition, perceived benefit and system quality. Finally, we build the adoption model based on the perspective of trust and distrust in order to provide a theoretical reference to mobile banking prolongation. Key words: Mobile banking; Adoption; Trust; Distrus

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

A Novel Microfluidic Flow Rate Detection Method Based on Surface Plasmon Resonance Temperature Imaging

A novel microfluidic flow rate detection method based on surface plasmon resonance (SPR) temperature imaging is proposed. The measurement is performed by space-resolved SPR imaging of the flow induced temperature variations. Theoretical simulations and analysis were performed to demonstrate a proof of concept using this approach. Experiments were implemented and results showed that water flow rates within a wide range of tens to hundreds of μL/min could be detected. The flow rate sensor is resistant to disturbances and can be easily integrated into microfluidic lab-on-chip systems

Study on the Pressure Drop Variation and Prediction Model of Heavy Oil Gas-Liquid Two-Phase Flow

To explore the pressure drop variation with the viscosity of heavy oil gas-liquid two-phase flow, experiments with different viscosity gas-liquid two-phase flows are carried out. The experimental results show that the total pressure drop increases with increasing liquid viscosity when the superficial gas and liquid flow rates are the same. The liquid superficial velocity is 0.52 m/s, and the superficial gas velocity is 12 m/s in the vertical and inclined pipes, as there is a negative friction pressure drop when the superficial gas and liquid velocities are small. Additionally, the increased range of the total pressure drop decreases with increasing liquid viscosity. Considering the heavy oil gas-liquid two-phase flow, a prediction model of the pressure drop in high-viscosity liquid-gas two-phase flow is established. The new model is verified by experimental data and compared with existing models. The new model has the smallest error, basically within 15%. Based on the prediction of the wellbore pressure distribution of four wells in the BeiA oilfield, the new model prediction results are closer to the measured results, and the error is the smallest. The new model can be used to predict pressure drops in high-viscosity gas-liquid two-phase flow

Cluster-based flow control in hybrid software-defined wireless sensor networks

Software-defined networking (SDN) is a cornerstone of next-generation networks and has already led to numerous advantages for data-center networks and wide-area networks. However, SDN is not widely adopted in constrained networks, such as Wireless Sensor Networks (WSN), due to excessive control overhead, lossy medium, and in-band control channels. Therefore, a key challenge to enable Software-Defined Wireless Sensor Networks (SD-WSN) is to reduce the number of control messages required to configure the data plane. In this paper, we propose a cluster-based flow control approach in hybrid SDNs. Our approach is hybrid in the sense that it takes advantage of distributed legacy routing and centralized SDN routing. In addition, it makes a trade-off between the granularity of flow control and the communication overhead induced by the SDN controller. The approach partitions a network into clusters with minimum number of border nodes. Instead of handling the individual flows of each node, the SDN controller only manages incoming and outgoing traffic flows of clusters through border nodes, while the flows inside each cluster are controlled by a distributed legacy WSN routing algorithm. Our proof-of-concept implementations in both software and hardware show that our approach is efficient with respect to reducing the number of nodes that must be managed and the number of control messages. In comparison to benchmark solutions with and without clustering, our solution reduces communication costs for flow configuration in an SD-WSN at least by 27% and at most by 88% respectively, without degrading packet delay nor delivery rate.</p

Experimental Study on Rock Mechanics Parameters-A Case of the Sand Conglomerate Reservoir in M2 Well Area

This paper presents the acoustic characteristics tested on 20 groups of cores (20 vertical samples and 60 horizontal samples) from the sand conglomerate reservoir in Baikouquan and lower Wuerhe Formation (two wells in the M2 well area). The average values of dynamic modulus of elasticity and Poisson's ratio of rocks from Baikouquan Formation are 32.1 GPa and 0.2055 respectively, and those of lower Wuerhe Formation are 28.4 GPa and 0.2425 respectively. The three axis rock mechanics test device is used to test the stress-strain curves of the corresponding rock samples. The sand-conglomerate samples in this area generally have good brittleness characteristics; the static modulus of elasticity and Poisson's ratio of the corresponding rock samples are 13.7GPa and 0.2858 respectively, and those of rocks from lower Wuerhe Formation are 14.9GPa and 0.2565, respectively. In general, there is a good correlation between P&S wave velocity, and poor correlation in the dynamic and static mechanical parameters