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Performance evaluation of China’s industry: a generalized equilibrium data envelopment analysis approach with fixed-sum undesirable output

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INFOR: Information Systems and Operational Research 2021年第2期59卷 290-308页

作者： Min Yang Qingxian An Dan Hu Liang Liang a School of Management Hefei University of Technology Hefei P.R. China b Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology Hefei P.R.China c School of Business Central South University Changsha P.R. China

With the rapid development of economy, China faces a considerable challenge from environmental problems especially with the waste of gas pollution emitted by China’s industry. To assess the environmental performance of China’s industry, this paper proposes a new generalized equilibrium efficiency frontier data envelopment analysis (GEEFDEA) approach that considers waste gases as undesirable outputs. It is assumed that the total amount of waste gases that the industry is permitted to emit is limited (or fixed), that is, increasing air pollution of one area should reduce that of other areas for offset. Compared with the prior evaluation approaches, the proposed approach is the only one that satisfies the following conditions simultaneously. 1) The approach considers the property of the “fixed sum” of undesirable outputs. 2) The approach avoids the case in prior approaches (e.g., fixed sum output and zero sum gains DEA approaches) that an infeasible solution may occur when accessing decision making unites (DMUs) with fixed-sum undesirable outputs. 3) The proposed approach inherits all advantages of the GEEFDEA approach such as achieving an equilibrium state by only one step and obtaining a complete ranking order. Finally, the proposed approach is applied to evaluate the performance in China’s industry from 2008 to 2013. Results show China’s industry has significant regional characteristics.

关键词： Generalized equilibrium efficient frontier data envelopment analysis fixed-sum undesirable outputs China’s industry

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Generation of optimal persistent formations for heterogeneous multi-agent systems with a leader constraint

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Chinese Physics B 2018年第2期27卷 645-654页

作者：王国强罗贺胡笑旋 School of Management Hefei University of Technology Key Laboratory of Process Optimization & Intelligent Decision-making Ministry of Education

In this study, we consider the generation of optimal persistent formations for heterogeneous multi-agent systems, with the leader constraint that only specific agents can act as leaders. We analyze three modes to control the optimal persistent formations in two-dimensional space, thereby establishing a model for their constrained generation. Then, we propose an algorithm for generating the optimal persistent formation for heterogeneous multi-agent systems with a leader constraint （LC-HMAS-OPFGA）, which is the exact solution algorithm of the model, and we theoretically prove its validity. This algorithm includes two kernel sub-algorithms, which are optimal persistent graph generating algorithm based on a minimum cost arborescence and the shortest path （MCA-SP-OPGGA）, and the optimal persistent graph adjusting algorithm based on the shortest path （SP-OPGAA）. Under a given agent formation shape and leader constraint, LC-HMAS-OPFGA first generates the network topology and its optimal rigid graph corresponding to this formation shape. Then, LC-HMAS- OPFGA uses MCA-SP-OPGGA to direct the optimal rigid graph to generate the optimal persistent graph. Finally, LC- HMAS-OPFGA uses SP-OPGAA to adjust the optimal persistent graph until it satisfies the leader constraint. We also demonstrate the algorithm, LC-HMAS-OPFGA, with an example and verify its effectiveness.

关键词： leader constraint heterogeneous multi-agent system optimal persistent formation minimum costarborescence

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A data-driven open decision framework based on adaptive evidential reasoning rule

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Computers and Industrial Engineering 2025年 206卷

作者： Fu, Chao Chang, Wenjun Xue, Min Lu, Guangyan 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 Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Anhui Hefei230009 China Department of Operation Management CRRC Qishuyan Instiute Co. Ltd Jiangsu Changzhou213011 China

Data accumulation is beneficial to efficiently make decisions on new cases. However, it is rare to use the historical data of experienced decision makers to help inexperienced decision makers generate solutions to new cases in an open and adaptive way. To address this issue, a data-driven open decision framework is developed based on an adaptive evidential reasoning (ER) rule. Giving two assumptions, criterion weights are learned from historical individual and overall assessments while criterion reliabilities are learned from historical individual assessments and gold standards. The adaptive ER rule is developed based on two abstract functions with the variables of criterion weight and criterion reliability, respectively, whose properties are analyzed. With these foundations, the general situation of the open decision framework is demonstrated from the learning, aggregation, and prediction hierarchies. Three special situations of the framework are presented to make it flexible. The historical data of more experienced decision makers are allowed in the framework to ensure its openness. The applicability of the general situation of the open decision framework is examined by its use in helping diagnose breast lesions. Its performance is highlighted by the comparison with the three special situations of the framework, five representative machine learning algorithms, and the framework with four forms of two abstract functions. © 2025 Elsevier Ltd

关键词： Learning algorithms

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Smart surgical telementoring system with patch-to-pixel point tracking method

EngMedicine

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EngMedicine 2025年第1期2卷

作者： Yanwei Qu Ling Li Tongtong Li Jieyu Zheng Wentao Tang Xiaojian Li School of Management Hefei University of Technology Hefei 230009 Anhui China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Ministry of Education Hefei 230009 Anhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 Anhui China Hefei Haochuan Information Technology Co. Ltd Hefei 230088 Anhui China Ministry of Education Engineering Research Center for Intelligent Decision-Making Information System Technologies Hefei 230009 Anhui China

The utilization of surgical telementoring has become increasingly prevalent in enhancing the surgical standards of grassroots hospitals in contemporary times. In the traditional framework of surgical telementoring, remote doctors guide grassroots doctors via video and audio communication, which may encounter obstacles such as inadequate information transfer and challenges in accurately pinpointing the surgical site and path. To mitigate these issues, this study introduces an intelligent surgical telementoring system based on edge computing. This system enables the transmission of points marked by remote doctors to grassroots doctors, updating these points’ coordinates in real-time on the local endoscopic video. In this system, a novel method named P2PTracking (Patch-to-Pixel Point Tracking) is implemented. This process begins with the tracking of a square patch surrounding the point marked by the remote doctor using SiameseFC. Following this, feature matching is performed on the tracked patch in the current frame and the square patch in the labelled frame or template frame. The affine transformation is then calculated based on the feature matching results. Lastly, the point tracking result is derived using the computed affine transformation. Experimental results indicate that the proposed system has a transmission speed of 1.99M/s and a transmission latency of 171 ms when transmitting video at a resolution of 1920 × 1080px, while the proposed method can achieve an accuracy of 96.6 % when the pixel error is 4. The code and data are available at https://***/hfut66/*** .

关键词： Surgical telementoring Patch tracking Point tracking Feature matching Template update mechanism

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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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E-Commerce and Internet Technology (ECIT), International Conference on

作者： Jianmin He Tingting Liu Yuanchun Jiang Ying Li Hao Wu 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: (数字)9781728159027

ISBN: (纸本)9781728159034

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.

关键词： Q & A community paid consulting services service influence social capital measurement method for service influence Social Capital Communities Quality factor Legal Services decision making support Quality of service Dataset

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Research on the Operational Efficiency of Subscription Accounts

Research on the Operational Efficiency of Subscription Accou...

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E-Commerce and Internet Technology (ECIT), International Conference on

作者： Jianmin He Hao Wu Tingting Liu 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: (数字)9781728159027

ISBN: (纸本)9781728159034

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.

关键词： subscription accounts operational efficiency DEA Tobit Subscriptions DEAS operating efficiency

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Independent or Social Driven decision? A Counterfactual Refinement Strategy for Graph-Based Social Recommendation

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ACM Transactions on Information Systems 2025年第3期43卷

作者： Li, Dongyang Sun, Jianshan Gao, Chongming Feng, Fuli Yuan, Kun School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China University of Science and Technology of China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China University of Science and Technology of China Hefei China Hefei University of Technology Hefei China Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management Hefei China

Social recommendation models have traditionally relied on social homophily to enhance user preference prediction by incorporating information from socially connected friends. However, this approach neglects the diverse nature of social relationships. Some individuals with independent personalities often prioritize their own interests over friends’ advice when making purchase decisions. Conversely, those who seek advice from others are more susceptible to social influence. Moreover, the existing methods tend to overlook redundant and noisy social relationships within the network, hindering their ability to achieve accurate recommendations. In response, this article proposes a novel counterfactual method to understand the causal factors driving purchase behaviors, thereby identifying the influence of users’ friends on their purchase decisions. By answering counterfactual questions about the influence of a friend’s purchase behavior on the user’s choices, we develop a causal model to represent social influence in the network. Our proposed refinement strategy, grounded in causal inference, generates counterfactual purchase behavior and guides the refinement of the social graph. Moreover, we present tailored graph refinement methods at various levels, ensuring fine-grained improvements. Experimental results on benchmark data demonstrate that the application of our strategy to different social recommendation models significantly enhances their predictive performance. The source code has been made available on https://***/LDY911/CFRSSR-Code. © 2025 Copyright held by the owner/author(s).

关键词： Sociology

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The Influencing Factors of sCER Price Dynamics Under the Clean Development Mechanism: Theory and Econometric Analysis

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Journal of Systems Science & Complexity 2018年第5期31卷 1244-1272页

作者： ZHANG Chen WU Yaqi YANG Yu School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization & Intelligent Decision Making of Ministry of Education Hefei 230009 China

In order to explore the factors and their complex mechanism affecting the price dynamics under the clean development mechanism （CDM）, this article employs the secondary Certified Emission Reduction （sCER） carbon price as the study object, and analyzes its influencing factors from aspects of the international carbon-reduction policies, macroeconomic fluctuations, energy and similar carbon products prices. The innovation of this paper lies in： Introducing necessary factor （the developing countries pricing power） and the application of several international representative indicators to un- derline the ＂world＂ nature of CDM; utilizing different econometric models to obtain noteworthy and more robust results. The authors test the theoretical findings with multiple stationary time series from the launch of CDM to present （2008-2016）. The results reveal that sCER price fluctuation shows the characteristic of asymmetry and substantial persistence. There is a strong statistically significant relationship between macroeconomic conditions, coal and oil prices, with the price of sCER. The authors discover that the pricing power of developing countries indeed has a clear but small impact on the sCER price changes, whereas the price elasticity of supply under CDM is so weak. The interaction between EU emission allowances （EUAs） and sCER presents a shift from dependency to substitution.

关键词： Clean development mechanism econometric analysis influencing factors sCER carbonprice.

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Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method

arXiv

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arXiv 2025年

作者： Yang, Yang Zhang, Tong Wu, Jian Su, Lijie National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China China Criminal Police University Shenyang China Center for Advanced Process Decision-making Carnegie Mellon University PittsburghPA United States Beijing Institute for General Artificial Intelligence Beijing China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University ChinaShenyang Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China

With the rapid advancement of large language models, academic topic identification and topic evolution analysis are crucial for enhancing AI’s understanding capabilities. Dynamic topic analysis provides a powerful approach to capturing and understanding the temporal evolution of topics in large-scale datasets. This paper presents a two-stage dynamic topic analysis framework that incorporates convex optimization to improve topic consistency, sparsity, and interpretability. In Stage 1, a two-layer non-negative matrix factorization (NMF) model is employed to extract annual topics and identify key terms. In Stage 2, a convex optimization algorithm refines the dynamic topic structure using the convex NMF (cNMF) model, further enhancing topic integration and stability. Applying the proposed method to IEEE journal abstracts from 2004 to 2022 effectively identifies and quantifies emerging research topics, such as COVID-19 and digital twins. By optimizing sparsity differences in the clustering feature space between traditional and emerging research topics, the framework provides deeper insights into topic evolution and ranking analysis. Moreover, the NMF-cNMF model demonstrates superior stability in topic consistency. At sparsity levels of 0.4, 0.6, and 0.9, the proposed approach improves topic ranking stability by 24.51%, 56.60%, and 36.93%, respectively. The source code is available at https://***/meetyangyang/CDNMF. © 2025, CC BY.

关键词： Non-negative matrix factorization

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Solving the Observing and Downloading Integrated Scheduling Problem of Earth Observation Satellite with a Quantum Genetic Algorithm

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Journal of Systems Science and Information 2018年第5期9卷 399-420页

作者： Ye ZHANG Xiaoxuan HU Waiming ZHU Peng JIN School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

This paper addresses the integrated Earth observation satellite scheduling problem. It is a complicated problem because observing and downloading operations are both involved. We use an acyclic directed graph model to describe the observing and downloading integrated scheduling *** on the model which considering energy constraints and storage capacity constraints, we develop an efficient solving method using a novel quantum genetic algorithm. We design a new encoding and decoding scheme that can generate feasible solution and increase the diversity of the *** results of the simulation experiments show that the proposed method solves the integrated Earth observation satellite scheduling problem with good performance and outperforms the genetic algorithm and greedy algorithm on all instances.

关键词： Earth observation satellite integrated scheduling quantum genetic algorithm

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