Co-creation, Failure Learning, and Relaunch Success: Evidence from Online Crowdfunding Market

With intense competition and relatively inexperienced founders, the crowdfunding market has reported high failure rates. However, the IT components of the crowdfunding market provide entrepreneurs with more opportunities for experimentation and trial, leading to a new phenomenon of post-failure relaunches. Research into campaign relaunch success is urgently needed but under-researched. By combining failure learning theory with a collective perspective, the present study examines how investors\u27 co-creation, in terms of advocacy and feedback, can benefit crowdfunding relaunch success directly or indirectly (by motivating founders\u27 failure learning). The study tested the proposed mediation model with 1,902 failure-relaunched Kickstarter campaigns, with most hypotheses supported. Furthermore, the study explores the role of the time interval between crowdfunding relaunch and prior release. The findings indicate that an increased time interval enhances the positive effects of founders\u27 learning efforts on relaunch success while attenuating the potential positive effects of investors\u27 advocacy, implying a tradeoff in timing decisions

Transient energy protection based on wavelet packet transform for hybrid bipolar HVDC transmission system

Abstract In hybrid bipolar DC transmission systems with different types of converters at each pole, the transient high‐frequency component of the voltage signal under a single‐pole grounding fault and an inter‐pole fault is significantly different for internal and external faults because of smooth‐wave reactors on both sides of the DC line. Based on these characteristics, a single‐ended electrical quantity protection scheme based on transient energy is proposed. First, the voltage fault component is extracted and then processed by using a wavelet packet transform to obtain the transient energy in each frequency band. Second, the protection criterion is determined based on the ratio between low‐frequency energy and the sum of high‐frequency energy. After the setting principle is given, the influence of the protection scheme under high transition resistance is analysed. The protection scheme is implemented in MATLAB and tested based on fault data obtained from a hybrid bipolar HVDC transmission model built in PSCAD under different operating conditions. The effectiveness of the proposed protection method is verified by simulation tests under different fault types at different fault distances. The proposed method can provide strong tolerance to high transient resistance, accurately identify internal/external faults and automatically identify fault poles

FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks

Mobile cloud computing has emerged as a promising paradigm to facilitate computation-intensive and delay-sensitive mobile applications. Computation offloading services at the edge mobile cloud environment are provided by small-scale cloud infrastructures such as cloudlets. While offloading tasks to in-proximity cloudlets enjoys benefits of lower latency and smaller energy consumption, new issues related to the cloudlets are rising. For instance, unbalanced task distribution and huge load gaps among heterogeneous mobile cloudlets are becoming challenging with respect to network dynamics and distributed task offloading. In this paper, we propose 'FairEdge', a Fairness-oriented computation offloading scheme to enable balanced task distribution for mobile Edge cloudlet networks. By integrating the balls-and-bins theory with fairness index, our solution promotes effective load balancing with limited information at low computation cost. The evaluation results from extensive simulations and experiments with real-world datasets show that FairEdge outperforms conventional task offloading methods, it can achieve a network fairness up to 0.85 and reduce the unbalanced task offload by 50%

A Consumer-tier based Visual-Brain Machine Interface for Augmented Reality Glasses Interactions

Objective.Visual-Brain Machine Interface(V-BMI) has provide a novel interaction technique for Augmented Reality (AR) industries. Several state-of-arts work has demonstates its high accuracy and real-time interaction capbilities. However, most of the studies employ EEGs devices that are rigid and difficult to apply in real-life AR glasseses application sceniraros. Here we develop a consumer-tier Visual-Brain Machine Inteface(V-BMI) system specialized for Augmented Reality(AR) glasses interactions. Approach. The developed system consists of a wearable hardware which takes advantages of fast set-up, reliable recording and comfortable wearable experience that specificized for AR glasses applications. Complementing this hardware, we have devised a software framework that facilitates real-time interactions within the system while accommodating a modular configuration to enhance scalability. Main results. The developed hardware is only 110g and 120x85x23 mm, which with 1 Tohm and peak to peak voltage is less than 1.5 uV, and a V-BMI based angry bird game and an Internet of Thing (IoT) AR applications are deisgned, we demonstrated such technology merits of intuitive experience and efficiency interaction. The real-time interaction accuracy is between 85 and 96 percentages in a commercial AR glasses (DTI is 2.24s and ITR 65 bits-min ). Significance. Our study indicates the developed system can provide an essential hardware-software framework for consumer based V-BMI AR glasses. Also, we derive several pivotal design factors for a consumer-grade V-BMI-based AR system: 1) Dynamic adaptation of stimulation patterns-classification methods via computer vision algorithms is necessary for AR glasses applications; and 2) Algorithmic localization to foster system stability and latency reduction.Comment: 15 pages,10 figure

Corrigendum: Estimating the Minimal Number of Repeated Examinations for Random Responsiveness With the Coma Recovery Scale-Revised as an Example.

[This corrects the article DOI: 10.3389/fnint.2021.685627.]