519 research outputs found
Passengers as defenders: unveiling the role of customer-company identification in the trust-customer citizenship behaviour relationship within ride-hailing context
No full textRide-hailing platforms such as Didi, Uber, and Lyft have changed the travel industry. Promoting the passengers' trust in platform and customer citizenship behaviour (CCB) is both challenging and important. This study employed a mixed-methods design, consisting of 21 interviews and 351 online surveys, to develop and examine the trust-CCB model in the ride-hailing context. Our findings reveal that platforms can foster passengers' trust by sending service-related signals (i.e., service quality and structure assurance) and a firm-related signal (i.e., platform reputation). Customer-company identification (CCI) mediates the relationship between passengers' trust and CCB, where passengers engage in CCB by providing recommendations, exhibiting forgiving behaviour, providing feedback, and participating in research in ride-hailing. Additionally, firm-related signals, including platform size and reputation, enhance the positive relationship between trust and CCI. These findings contribute to the body of knowledge on trust, CCB, and signaling theory, providing potential practical implications for ride-hailing platforms.</p
Designing a talents training model for cross-border e-commerce: a mixed approach of problem-based learning with social media
No full text© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Cross-border e-commerce has developed rapidly integrating the global economy. Research has presented some solutions for the challenges and barriers in cross-border e-commerce from the perspective of the enterprise. However, little is known about the requirements of cross-border e-commerce talents and how to train them. In this paper, we firstly conducted semi-structured interviews to acquire the requirements of cross-border e-commerce talents. Business and market knowledge, technical skills, analytical ability and business practical ability were found to be the four core requirements. Then, we integrated problem-based learning and social media to design a talents training model for cross-border e-commerce and did a program to evaluate effectiveness of the model. Finally, its effectiveness was evaluated from the four evaluation dimensions of attitude, perceived enjoyment, concentration and work intention. The talents training model was improved according to the suggestions
Achieving employees’ agile response in e-governance: exploring the synergy of technology and group collaboration
No full textThe transformation of technology and collaboration methods driven by the e-government system forces government employees to reconsider their daily workflow and collaboration with colleagues. Despite the extensive existing knowledge of technology usage and collaboration, there are limitations in explaining the synergy between technology usage and group collaboration in achieving agile response from the perspective of government employees, particularly in the e-government setting. To address these challenges, this study provides a holistic understanding of the successful pathway to agile response in e-governance from the perspective of government employees. This study explores a dual path to achieve agile response in e-governance through qualitative analysis, involving 34 in-depth semi-structured interviews with government employees in several government sectors in China. By employing three rounds of coding processes and adopting Interpretative Structural Modeling (ISM), this study identifies the five-layer mechanisms leading to agile response in e-governance, considering both government employee technology usage and group collaboration perspectives. Findings of this study provides suggestions and implications for achieving agile response in e-governance.</p
Fault detection and fault-tolerant control for nonlinear systems
No full textLinlin Li addresses the analysis and design issues of observer-based FD and FTC for nonlinear systems. The author analyses the existence conditions for the nonlinear observer-based FD systems to gain a deeper insight into the construction of FD systems. Aided by the T-S fuzzy technique, she recommends different design schemes, among them the L_inf/L_2 type of FD systems. The derived FD and FTC approaches are verified by two benchmark processes. Contents Overview of FD and FTC Technology Configuration of Nonlinear Observer-Based FD Systems Design of L2 nonlinear Observer-Based FD Systems Design of Weighted Fuzzy Observer-Based FD Systems FTC Configurations for Nonlinear Systems< Application to Benchmark Processes Target Groups Researchers and students in the field of engineering with a focus on fault diagnosis and fault-tolerant control fields The Author Dr. Linlin Li completed her dissertation under the supervision of Prof. Steven X. Ding at the Faculty of Engineering, University of Duisburg-Essen, Germany
Aerodynamic force breakdown based on vortex force theory
No full textA recently proposed aerodynamic force theory of compressible high Reynolds number flows based on the concept of vortex force is here analyzed. The aerodynamic force is obtained by means of volume and surface integrals within the flow. The theory proposes the decomposition of the aerodynamic force, both lift and drag components, in reversible and irreversible contri- butions. The former is responsible for the lift and lift induced drag, the latter for the profile and wave drag components. The analysis is here concentrated on the following aspects: a) sensitivity of the force decomposition to the choice of the integration domain, b) analysis in the limits of infinite Reynolds number. The verification of the analysis is obtained post-processing inviscid and viscous numerical solutions around airfoils and wings
Influence of third body evolution on tribological property of copper-matrix friction material by surface treatment
No full textDeep Image Prior for Disentangling Mixed Pixels
No full textA mixed pixel in remotely sensed images measures the reflectance and emission from multiple target types (e.g., tree, grass, and building) from a certain area. Mixed pixels exist commonly in spaceborne hyper-/multi-spectral images due to sensor limitations, causing the signature ambiguity problem and impeding high-resolution remote sensing mapping. Disentangling mixed pixels into the underlying constituent components is a challenging ill-posed inverse problem, which requires efficient modeling of spatial prior information and other application-dependent prior knowledge concerning the mixed pixel generation process.
The recent deep image prior (DIP) approach and other application-dependent prior information are integrated into a Bayesian framework in the research, which allows comprehensive usage of different prior knowledge.
The research improves mixed pixel disentangling using the Bayesian DIP in three key applications: spectral unmixing (SU), subpixel mapping (SPM), and soil moisture product downscaling (SMD).
The main contributions are summarized as follows.
First, to improve the decomposition of mixed pixels into pure material spectra (i.e., endmembers) and their constituting fractions (i.e., abundances) in SU, a designed deep fully convolutional neural network (DCNN) and a new spectral mixture model (SMM) with heterogeneous noise are integrated into a Bayesian framework that is efficiently solved by a new iterative optimization algorithm.
Second, to improve the decomposition of mixed pixels into class labels of subpixels in SPM, a dedicated DCNN architecture and a new discrete SMM are integrated into the Bayesian framework to allow the use of both spatial prior and the forward model.
Third, to improve the decomposition of mixed pixels into soil moisture concentrations of subpixels in SMD, a new DIP architecture and a forward degradation model are integrated into the Bayesian framework that is solved by the stochastic gradient descent approach.
These new Bayesian approaches improve the state-of-the-art in their respective applications (i.e., SU, SPM, and SMD), which can be potentially utilized for solving other ill-posed inverse problems where simultaneously modeling of the spatial prior and other prior knowledge is needed
A safety investment optimization model for power grid enterprises based on System Dynamics and Bayesian network theory
No full textIn recent years, frequent large-scale power grid accidents have caused serious economic losses and bad social impact, which has drawn great attention from power grid enterprises. As one of the key elements of production, safety investment plays an important role in improving the safety level and reducing accident loss. In this paper, System dynamics (SD) and Bayesian network (BN) are integrated to develop a novel safety investment optimization model for power grid enterprises, which takes into account the impact of safety investment factors on accidents and the interactions between them. Based on sensitivity analysis, critical safety investment factors are determined to form the subsystem of the SD model. Subsequently, the optimal safety investment strategy is determined by a three-step simulation. The simulation results show that there are barrel effects and a diminishing marginal utility in safety investment. The proposed safety investment optimization model is practical to provide technical supports and guidance for determining an effective safety investment strategy in power grid enterprises.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Safety and Security Scienc
Empirical-Likelihood-Based Inference for Partially Linear Models
No full textPartially linear models find extensive application in biometrics, econometrics, social sciences, and various other fields due to their versatility in accommodating both parametric and nonparametric elements. This study aims to establish statistical inference for the parametric component effects within these models, employing a nonparametric empirical likelihood approach. The proposed method involves a projection step to eliminate the nuisance nonparametric component and utilizes an empirical-likelihood-based technique, along with the Bartlett correction, to enhance the coverage probability of the confidence interval for the parameter of interest. This method demonstrates robustness in handling normally and non-normally distributed errors. The proposed empirical likelihood ratio statistic converges to a limiting chi-square distribution under certain regulations. Simulation studies demonstrate that this method provides better inference in terms of coverage probabilities compared to the conventional normal-approximation-based method. The proposed method is illustrated by analyzing the Boston housing data from a real study
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