Revisiting customer loyalty toward mobile e-commerce in the hospitality industry: does brand viscosity matter?

Purpose To better understand how to retain hospitality customers in the fierce competition among mobile applications, this study aims to propose and empirically validates an integrative framework, which elaborates how conscious and subconscious factors, together with affective factors, may induce app loyalty and how brand viscosity moderates such effects. Design/methodology/approach The authors conducted an online survey to collect data and received a total of 268 valid responses. This study splits the data into two groups (brand viscosity vs non-viscosity). Then, the authors performed a multi-group structural equation modeling with Chi-square difference tests to compare the model between the two groups. Findings The findings support the integrative model and reveal that the influence of app satisfaction on loyalty is stronger for app users who do not stick to one brand across the website and mobile app channels. Moreover, for those with brand viscosity, habit and switching cost are two significant determinants that exert positive effects in inducing app loyalty. Research limitations/implications Brand viscosity across different channels matters for the effects of habit and switching costs in shaping app loyalty. E-commerce managers should elaborate on brand management among various booking channels and establish effective digital marketing strategies to facilitate the formation of usage habits and switching costs and to enhance brand viscosity across channels. Originality/value This research advances the knowledge of app loyalty in hospitality by providing a comprehensive explanatory framework from affective, conscious and subconscious lenses. This research is among the first to unveil the impact of brand viscosity on the links between loyalty and its determinants

Accurate Time-segmented Loss Model for SiC MOSFETs in Electro-thermal Multi-Rate Simulation

Compared with silicon (Si) power devices, Silicon carbide (SiC) devices have the advantages of fast switching speed and low on-resistance. However, the effects of non-ideal characteristics of SiC MOSFETs and stray parameters (especially parasitic inductance) on switching losses need to be further evaluated. In this paper, a transient loss model based on SiC MOSFET and SiC Schottky barrier diode (SBD) switching pairs is proposed. The transient process analysis is simplified by time segmentation of the transient process of power switching devices. The electro-thermal simulation calculates the junction temperature and updates the temperature-related parameters with the proposed loss model and the thermal network model. A multi-rate data exchange strategy is proposed to solve the problem of disparity in timescales between circuit simulation and thermal network simulation. The CREE CMF20120D SiC MOSFET device is used for the experimental verification. The experimental results verify the accuracy of the model which provides guidance for the circuit design of SiC MOSFETs. All the parameters of the loss model can be extracted from the datasheet, which is practical in power electronics design

Characteristics and Mitigation Measures of Aircraft Pollutant Emissions at Nanjing Lukou International Airport (NKG), China

The assessment of local air pollution due to aircraft emissions at/near the airport is an important issue from the standpoint of environment and human health, but has not received due attention in China. In this paper, the pollutant emissions (i.e. HC, CO, NOx, SOx and PM) from aircraft during landing and take-off (LTO) cycles at Nanjing Lukou Airport (NKG) in 2016 were investigated using an improved method, which considered the taxi-in and –out time calculated based on the real data from the Civil Aviation Administration of China (CAAC), instead of using the referenced time recommended by ICAO. First, the pollutant emissions and their characteristics were studied from different perspectives. Second, two various mitigation measures of emissions were proposed, and the performance of emission reduction was analysed. Our analysis shows that: (1) A320 and B738 emitted the largest emissions at NKG; (2) pollutants were mainly emitted during the taxi mode, followed by climb mode; (3) B738 had the lowest emissions per (seat•LTO) among all aircraft, while CRJ had the lowest emissions per unit LTO; (4) shortening the taxiing time and upgrading aircraft engines are both effective measures to mitigate pollutant emissions.</p

Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning

Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene. However, the existing methods are devoted to combing diverse emotion cues while ignoring the inherent uncertainties under unconstrained environments, such as congestion and occlusion occurring within a group. Additionally, since only group-level labels are available, inconsistent emotion predictions among individuals in one group can confuse the network. In this paper, we propose an uncertainty-aware learning (UAL) method to extract more robust representations for GER. By explicitly modeling the uncertainty of each individual, we utilize stochastic embedding drawn from a Gaussian distribution instead of deterministic point embedding. This representation captures the probabilities of different emotions and generates diverse predictions through this stochasticity during the inference stage. Furthermore, uncertainty-sensitive scores are adaptively assigned as the fusion weights of individuals' face within each group. Moreover, we develop an image enhancement module to enhance the model's robustness against severe noise. The overall three-branch model, encompassing face, object, and scene component, is guided by a proportional-weighted fusion strategy and integrates the proposed uncertainty-aware method to produce the final group-level output. Experimental results demonstrate the effectiveness and generalization ability of our method across three widely used databases.Comment: 11 pages,3 figure

FPGA-Based Implicit-Explicit Real-time Simulation Solver for Railway Wireless Power Transfer with Nonlinear Magnetic Coupling Components

Railway Wireless Power Transfer (WPT) is a promising non-contact power supply solution, but constructing prototypes for controller testing can be both costly and unsafe. Real-time hardware-in-the-loop simulation is an effective and secure testing tool, but simulating the dynamic charging process of railway WPT systems is challenging due to the continuous changes in the nonlinear magnetic coupling components. To address this challenge, we propose an FPGA-based half-step implicit-explicit (IMEX) simulation solver. The proposed solver adopts an IMEX algorithm to solve the piecewise linear and nonlinear parts of the system separately, which enables FPGAs to solve nonlinear components while achieving high numerical stability. Additionally, we divide a complete integration step into two half-steps to reduce computational time delays. Our proposed method offers a promising solution for the real-time simulation of railway WPT systems. The novelty of our approach lies in the use of the IMEX algorithm and the half-step integration method, which significantly improves the accuracy and efficiency of the simulation. Our simulations and experiments demonstrate the effectiveness and accuracy of the proposed solver, which provides a new approach for simulating and optimizing railway WPT systems with nonlinear magnetic coupling components

An Event-Based Synchronization Framework for Controller Hardware-in-the-loop Simulation of Electric Railway Power Electronics Systems

The Controller Hardware_in_the_loop (CHIL) simulation is gaining popularity as a cost_effective, efficient, and reliable tool in the design and development process of fast_growing electrified transportation power converters. However, it is challenging to implement the conventional CHIL simulations on the railway power converters with complex topologies and high switching frequencies due to strict real_time constraints. Therefore, this paper proposes an event-based synchronization CHIL (ES_CHIL) framework for high_fidelity simulation of these electrified railway power converters. Different from conventional CHIL simulations synchronized through the time axis, the ES_CHIL framework is synchronized through the event axis. Therefore, it can ease the real_time constraint and broaden the upper bound on the system size and switching frequency. Besides, models and algorithms with higher accuracy, such as the diode model with natural commutation processes, can be used in the ES-CHIL framework. The proposed framework is validated for a 350 kW wireless power transformer system containing 24 fully controlled devices and 36 diodes by comparing it with Simulink and physical experiments. This research improves the fidelity and application range of the power converters CHIL simulation. Thus, it helps to accelerate the prototype design and performance evaluation process for electrified railways and other applications with such complex converters