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Why do you resist digital resurrection? Exploring the barriers affecting consumer resistance to digital resurrection technology

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Telematics and Informatics 2025年 101卷

作者： Changyong Liang Yuguang Xie Peiyu Zhou School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education Hefei China

With the advancement of digital technologies, digital resurrection, a technology that enables the recreation of deceased individuals in a digital form, has gained increasing attention. However, consumer resistance remains a significant barrier to widespread adoption despite its potential benefits. While prior studies have emphasized technical and ethical considerations, less focus has been placed on consumer resistance. This research fills that gap by employing the innovation resistance theory to explore the key factors driving resistance to digital resurrection technology. Specifically, we explore the role of functional, psychological, and social barriers on consumer resistance and examine the moderating roles of informational familiarity and intimacy with the deceased. Using stepwise hierarchical regression analyses on 319 survey responses, our results indicate that value, risk, tradition, image barriers, and social norms are significant drivers of consumer resistance. Moreover, informational familiarity reduces the impact of value barriers on resistance, while intimacy with the deceased reduces the impact of tradition barriers on resistance. This study extends innovation resistance theory to digital resurrection and incorporates social norms, providing deeper insights into the mechanisms of consumer resistance. The findings offer practical implications for technology developers and marketers to better understand and mitigate the barriers to adopting digital resurrection.

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Fast Construction Algorithm of Cooperative Relay Network for Unmanned Aerial Vehicle

Fast Construction Algorithm of Cooperative Relay Network for...

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第33届中国控制与决策会议

作者： Guoqiang Wang Pengfei Cheng He Luo School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Engineering Research Center for Intelligent Decision-making & Information Systems Technologies Ministry of Education Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security of China

In view of the situation that the relay network needs to be constructed between the combat nodes with communication interruption in battlefield environment,a fast construction algorithm of Unmanned Aerial Vehicle(UAV) cooperative relay network is proposed,which is based on the theory of multiple shortest paths and strategy of giving priority to the relay placement node that is the most difficult to reach for *** with the existing algorithms,this algorithm can further shorten the construction time of UAV cooperative relay network on the premise of minimizing the number of UAVs used for ***,the simulation experiment results demonstrate the effectiveness of the proposed algorithm.

关键词： UAV Relay network Node Placement Multiple shortest paths

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Research on the operational efficiency of subscription accounts

Research on the operational efficiency of subscription accou...

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2020 International Conference on E-Commerce and Internet Technology, ECIT 2020

作者： He, Jianmin Wu, Hao Liu, Tingting Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei China School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781728159027

The operation and profit of the subscription account is an important guarantee for maintaining its own high-quality development. It constructs an operation efficiency evaluation index system, with customer acquisition input and profit acquisition input as input indicators, and customer acquisition output and profit acquisition output as output indicators. On this basis, the DEA-BCC model was used to evaluate the operational efficiency of 28 subscription accounts. Based on the analysis results, this paper puts forward the improvement measures for non-DEA effective subscription accounts. Finally, this article uses Tobit regression to explore the influencing factors of operational efficiency. The results show that only 14 subscription accounts are DEA efficiency. The quality of average non-original articles has a negative impact on operational efficiency, and the quality of average original articles and the number of daily non-original articles have a positive impact on operational efficiency. Therefore, subscription accounts should pay attention to the quality of original articles and balance the quantity and quality of articles in operation management. © 2020 IEEE.

关键词： Profitability

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Measurement method of answerer service influence for Q & a community

Measurement method of answerer service influence for Q & a c...

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2020 International Conference on E-Commerce and Internet Technology, ECIT 2020

作者： He, Jianmin Liu, Tingting Jiang, Yuanchun Li, Ying Wu, Hao Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei China School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781728159027

In this paper, we explore the method of measuring answerer service influence on Q & A community in order to provide decision support for communities to obtain paid consultation questioners and high service influence answerers. From the social capital measuring angle, this paper selects three-dimensional indicators which contain unpaid services, paid services and services quality to measure answerer service influence on Q & A community. Accordingly, we construct a social capital measurement model method for answerer service influence and demonstrate its practical effectiveness. This method can help communities discover high service influence answerers, and formulate service strategies that motivate answerers and questioners to improve the overall service efficiency and effectiveness of the community. The numerical study on the dataset from *** indicates that the proposed method can obtain reasonable measurement results. © 2020 IEEE.

关键词： Numerical methods

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A quantitative pairwise comparison-based constraint handling technique for constrained optimization

arXiv

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arXiv 2023年

作者： Huang, Ting Zhang, Qiang Pedrycz, Witold Yang, Shanlin School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei Box 270 Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Anhui Hefei230009 China Department of Electrical & Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada

This study proposes a new constraint handling technique for assisting metaheuristic optimization algorithms to solve constrained optimization problems more effectively and efficiently. Given any two solutions of any constrained optimization problems, they are first mapped into a two-dimensional Cartesian coordinate system with their objective function value differences and constraint violation differences as the two axes. To the best of our knowledge, we are the first to deal with constraints by building such a Cartesian coordinate system. Then, the Cartesian coordinate system is divided into a series of grids by assigning ranks to different intervals of differences. In this way, a pairwise comparison criterion is derived with the use of the fused ranks, which achieves non-hierarchical comparison neither preferring objective function values nor constraint violations, resulting in more accurate evaluation compared with existing techniques. Moreover, an evaluation function that is equivalent to the pairwise comparison criterion is proposed, which further improves computational efficiency. The effectiveness and efficiency of the proposed constraint handling technique are verified on two well-known public datasets, that is, CEC 2006 and CEC 2017. The results demonstrate that metaheuristic optimization algorithms with using the proposed constraint handling technique can converge to a feasible optimal solution faster and more reliably. Experimental analysis on the parameters involved reveal guidance for their optimal *** Codes 90-08 (Primary) Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

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Optimal Sizing of Ev Charging Stations Considering Charging Scheduling and Renewable Energy

SSRN

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SSRN 2022年

作者： Zhou, Kaile Hu, Dingding Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education Hefei University of Technology Hefei230009 China National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China

The construction of electric vehicle (EV) charging stations is critical for the widespread use of EVs. Appropriate design of EV charging stations should fulfill charging requests while considering various economic issues. In this regard, we present an optimization model to address the sizing problem of EV charging stations with integration of renewable energy resources, energy storage system and multi-types of chargers. The proposed model aims at reducing the investment cost and improving the profitability of EV charging stations. In order to accurately estimate EV charging demand, the characteristics of EV charging behaviors are derived based on real-world charging data. Furthermore, a charging scheduling strategy that responds to time-of-use (TOU) price and charging demand levels is proposed to improve the utilization efficiency of chargers. Results show that the design of EV charging stations with integrated consideration of these components and charging scheduling strategy can achieve optimal economic efficiency. © 2022, The Authors. All rights reserved.

关键词： Renewable energy resources

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Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature

Dynamic Topic Model for Tracking Topic Evolution and Measuri...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yezheng Liu Jicheng Wang Yang Qian Yuanchun Jiang Jianshan Sun Yidong Chai School of Management Hefei University of Technology National Engineering Laboratory for Big Data Distribution and Exchange Technologies Key Laboratory of Process Optimization and Intelligent Decision Making Ministry of Education

ISBN: (纸本)9781665418164

With the progress of science and technology, a large number of scientific papers are published every year. Faced with such large data, identifying high-value research and hot research directions has become an interesting and important task. Previous studies focus less on the popularity measurement of research papers. In this paper, we use a dynamic topic model (DTM) for tracking the topic evolution trend and measuring the popularity of research papers based on the topic hotness. To test our model effectiveness, we conduct experiments on 5,964 research papers in the category of management. Our results show that DTM can help to monitor topic evolution and measuring popularity for scientific literature.

关键词： Current measurement Conferences Cyberspace Data science Market research Data models Time factors

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Emotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network

arXiv

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arXiv 2024年

作者： Jing, Erkang Liu, Yezheng Chai, Yidong Yu, Shuo Liu, Longshun Jiang, Yuanchun Wang, Yang School of Management Hefei University of Technology Anhui Hefei China School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Anhui Hefei China School of Management Hefei University of Technology Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management Anhui Hefei China Rawls College of Business Texas Tech University LubbockTX United States School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China

Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://***/jingrk/HDBN. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Probabilistic Runoff Forecasting Considering Stepwise Decomposition Framework and External Feature Integration Structure

SSRN

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SSRN 2023年

作者： Cao, Chaojin He, Yaoyao Cai, Siyu School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology Ministry of Education Hefei230009 China Anhui Key Laboratory of Philosophy and Social Sciences of Energy and Environment Smart Management and Green Low Carbon Development Hefei University of Technology Hefei230009 China China Institute of Water Resources and Hydropower Research Beijing100038 China

As dramatic weather changes complexify the change of runoff and introduce significant uncertainties, probabilistic runoff forecasting becomes increasingly important for water resource managers because it can quantify the uncertainty in the prediction process. However, little research has been conducted on how to employ decomposition techniques rationally in probabilistic prediction while effectively considering external factors. To fill the gap, this paper proposes a novel framework for probabilistic runoff forecasting, which incorporates several key components. It first utilizes the stepwise decomposition (SD) technique to prevent the information leakage problem. Then an external feature integration structure is designed to enhance the learning ability of the neural network for various features and decomposition sequences. The boundary correction technique is implemented to mitigate the boundary distortion problem. This comprehensive framework is named SDIFC. Building upon this framework, we achieve a synergistic integration of three distinct methods: seasonal-trend decomposition procedures based on loess (STL), least absolute shrinkage and selection operator (LASSO), and quantile regression long short-term memory (QRLSTM). This method is referred to as STL-LASSO-QRLSTM. We employ two datasets from the Lutaizi and Wangjiaba stations in China to conduct case studies. Several hybrid models and comparison frameworks are developed to conduct adequate comparative experiments, which are evaluated using six metrics that measure deterministic and probabilistic performance. The findings show that the predictive power of the proposed model exceeds comparative methods based on the normal and SD frameworks. This research holds immense importance for developing probabilistic models based on deep learning for runoff forecasting. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Optimal condition-based replacement policy with unknown degradation parameters

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Reliability Engineering & System Safety 2025年 264卷

作者： Rui Zheng Kai Wu Shaojun Lu Mengmeng Li School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology Hefei 230009 China

Condition-based maintenance (CBM) has been widely applied with the development of sensor technology. A commonly used assumption of CBM models is that the degradation processes have predetermined parameters. In many practical situations, however, the parameters of degradation processes vary from system to system due to heterogeneous environments and operation habits. This paper proposes a condition-based replacement policy for a system with the degradation process unspecified. The degradation of the system conforms to a gamma process with an unknown scale parameter, whose prior distribution follows a gamma distribution. Condition monitoring is performed at equidistant time epochs to reveal the degradation level. When the degradation level exceeds a critical threshold, a system failure occurs, triggering a corrective replacement. Otherwise, a decision must be made between no replacement or preventive replacement to minimize the long-run expected average cost per unit time. A Bayesian method is used to update the probability density of the unknown scale parameter based on monitored information. The optimization problem is formulated as a Markov decision process and solved by a policy iteration algorithm. A numerical example is provided to demonstrate the efficiency of the proposed policy.

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