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Professor Shengxiang Yang

Job: Professor of Computational Intelligence, Director of the Centre for Computational Intelligence (CCI)

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH UK

T: +44 (0)116 207 8805

E: syang@dmu.ac.uk

W: http://www.tech.dmu.ac.uk/~syang/

 

Personal profile

Shengxiang Yang is Professor of Computational Intelligence and Director of the Centre of Computational Intelligence (CCI), De Montfort University. Before joining the CCI in July 2012, he worked at Brunel University, University of Leicester, and King's College London as a Senior Lecturer, Lecturer, and Post-doctoral Research Associate, respectively.

Shengxiang's main research interests lie in evolutionary computation. He is particularly active in the area of evolutionary computation in dynamic and uncertain environments. Shengxiang has also published on the application of evolutionary computation in communication networks, logistics, transportation systems, and manufacturing systems, etc.

Research group affiliations

Centre for Computational Intelligence

Publications and outputs 

  • Dealing with scarce labelled data: Semi-supervised deep learning with mix match for Covid-19 detection using chest X-ray images
    Dealing with scarce labelled data: Semi-supervised deep learning with mix match for Covid-19 detection using chest X-ray images Calderon-Ramirez, Saul; Giri, Raghvendra; Yang, Shengxiang; Moemeni, Armaghan; Umana, Mario; Elizondo, David; Torrents-Barrena, Jordina; Molina-Cabello, Miguel A. Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently available tests are scarce and limited to people in danger of serious illness. The application of deep learning to chest X-ray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed. We develop and test a semi-supervised deep learning framework based on the Mix Match architecture to classify chest X-rays into Covid-19, pneumonia and healthy cases. The presented approach was calibrated using two publicly available datasets. The results show an accuracy increase of around 15% under low labelled / unlabelled data ratio. This indicates that our semi-supervised framework can help improve performance levels towards Covid-19 detection when the amount of high-quality labelled data is scarce. Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison. The file attached to this record is the author's final peer reviewed version.
  • Achievement scalarizing function sorting for strength Pareto evolutionary algorithm in many-objective optimization
    Achievement scalarizing function sorting for strength Pareto evolutionary algorithm in many-objective optimization Li, Xin; Li, Xiaoli; Wang, Kang; Yang, Shengxiang; Li, Yang Multi-objective evolutionary algorithms (MOEAs) have proven their effectiveness in solving two or three objective problems. However, recent research shows that Pareto-based MOEAs encounter selection difficulties facing many similar non-dominated solutions in dealing with many-objective problems. In order to reduce the selection pressure and improve the diversity, we propose achievement scalarizing function sorting strategy to make strength Pareto evolutionary algorithm suitable for many-objective optimization. In the proposed algorithm, we adopt density estimation strategy to redefine a new fitness value of a solution, which can select solution with good convergence and distribution. In addition, a clustering method is used to classify the non-dominated solutions, and then, an achievement scalarizing function ranking method is designed to layer different frontiers and eliminate redundant solutions in the environment selection stage, thus ensuring the convergence and diversity of non-dominant solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems with 3, 5, 8, 10 objectives. Experimental studies demonstrate that the proposed algorithm shows very competitive performance. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Handling constrained many-objective optimization problems via problem transformation
    Handling constrained many-objective optimization problems via problem transformation Jiao, Ruwang; Zeng, Sanyou; Li, Changhe; Yang, Shengxiang; Ong, Yew-Soon Objectives optimization and constraints satisfaction are two equally important goals to solve constrained many-objective optimization problems (CMaOPs). However, most existing studies for CMaOPs can be classified as feasibility-driven constrained many-objective evolutionary algorithms (C-MaOEAs), they always give priority to satisfy constraints, while ignoring the maintenance of the population diversity for dealing with conflicting objectives. Consequently, the population may be pushed towards some locally feasible optimal or locally infeasible areas in the high-dimensional objective space. To alleviate this issue, this paper presents a problem transformation technique, which transforms a CMaOP into a dynamic CMaOP (DCMaOP) for handling constraints and optimizing objectives simultaneously, to help the population cross the large and discrete infeasible regions. The well-known reference-point-based NSGA-III is tailored under the problem transformation model to solve CMaOPs, namely DCNSGA-III. In this paper, ε -feasible solutions play an important role in the proposed algorithm. To this end, in DCNSGA-III, a mating selection mechanism and an environmental selection operator are designed to generate and choose high-quality ε-feasible offspring solutions, respectively. The proposed algorithm is evaluated on a series of benchmark CMaOPs with 3, 5, 8, 10, and 15 objectives and compared against six state-of-the-art CMaOEAs. The experimental results indicate that the proposed algorithm is highly competitive for solving CMaOPs. The file attached to this record is the author's final peer reviewed version.
  • A prediction strategy based on decision variable analysis for dynamic multi-objective optimization
    A prediction strategy based on decision variable analysis for dynamic multi-objective optimization Zheng, Jinhua; Zhou, Yubing; Zou, Juan; Yang, Shengxiang; Ou, Junwei; Hu, Yaru Many multi-objective optimization problems in reality are dynamic, requiring the optimization algorithm to quickly track the moving optima after the environment changes. Therefore, response strategies are often used in dynamic multi-objective algorithms to find Pareto optimal. In this paper, we propose a hybrid prediction strategy based on the classification of decision variables, which consists of three steps. After detecting the environment change, the first step is to analyze the influence of each decision variable on individual convergence and distribution in the new environment. The second step is to adopt different prediction methods for different decision variables. Finally, adaptive selection is applied to the solution set generated in the first and second steps, and solutions with good convergence and diversity are selected to make the initial population more adaptable to the new environment. The prediction strategy can help the solution set converge while maintaining its diversity. The experimental results and performance show that the proposed algorithm is capable of significantly improving the dynamic optimization performance compared with five state-of-the-art evolutionary algorithms. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A many-objective evolutionary algorithm based on rotation and decomposition
    A many-objective evolutionary algorithm based on rotation and decomposition Zou, Juan; Liu, Jing; Yang, Shengxiang; Zheng, Jinhua Evolutionary algorithms have shown their promise in addressing multiobjective problems (MOPs). However, the Pareto dominance used in multiobjective optimization loses its effectiveness when addressing many-objective problems (MaOPs), which are defined as having more than three objectives. This is because the Pareto dominance loses its ability to distinguish between individuals. In this paper, a many-objective evolutionary algorithm based on rotation and decomposition is proposed (MaOEA-RD) to overcome the shortcoming of insufficient selection pressure caused by the Pareto dominance. First, the coordinates system is rotated and a hyperplane is established to distinguish between the nondominated individuals. Then, a novel individual selection mechanism incorporating decomposition is adopted to maintain the diversity of the population. In order to compensate for the deficiency of the predefined reference vectors, a reference vector adjustment mechanism is proposed. Experimental studies on several well-known benchmark problems show that the proposed algorithm is competitive compared with seven state-of-the-art many-objective algorithms. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Analysis and multi-objective optimization of slag powder process
    Analysis and multi-objective optimization of slag powder process Li, Xiaoli; Shen, Shiqi; Yang, Shengxiang; Wang, Kang; Li, Yang Slag powder is a process with characters of multivariables, strongly coupling and nonlinearity. The material layer thickness plays an important role in the process. It can reflect the dynamic balance between the feed volume and discharge volume in the vertical mill. Keeping the material layer thickness in a suitable range can not only improve the quality of powder, but also save electrical power. Previous studies on the material layer thickness did not consider the relationship among the material layer thickness, quality and yield. In this paper, the yield and quality factors are taken into account and the variables that affect the material layer thickness, yield and quality are analyzed. Then the models of material layer thickness, yield and quality are established based on generalized regression neural network. The production process demands for highest yield, best production quality and smallest error of material layer thickness at the same time. From this point of view, the slag powder process can be regarded as a multi-objective optimization problem. To improve the diversity of solutions, a CT-NSGAII algorithm is proposed by introducing the clustering-based truncation mechanism into solution selection process. Simulation shows that the proposed method can solve the multi-objective problem and obtain solutions with good diversity. open access article
  • An evolutionary approach to multiparty multiobjective optimization problems with common Pareto optimal solutions
    An evolutionary approach to multiparty multiobjective optimization problems with common Pareto optimal solutions Liu, Wenjie; Luo, Wenjian; Lin, Xin; Li, Miqing; Yang, Shengxiang Somereal-world optimization problems involve multiple decision makers holding different positions, each of whom has multiple conflicting objectives. These problems are defined as multiparty multiobjective optimization problems (MPMOPs). Although evolutionary multiobjective optimization has been widely studied for many years, little attention has been paid to multiparty multiobjective optimization in the field of evolutionary computation. In this paper, a class of MPMOPs, that is, MPMOPs having common Pareto optimal solutions, is addressed. A benchmark for MPMOPs, obtained by modifying an existing dynamic multiobjective optimization benchmark, is provided, and a multiparty multiobjective evolutionary algorithm to find the common Pareto optimal set is proposed. The results of experiments conducted using the benchmark show that the proposed multiparty multiobjective evolutionary algorithm is effective. The file attached to this record is the author's final peer reviewed version.
  • Particle swarm optimisation for scheduling electric vehicles with microgrids
    Particle swarm optimisation for scheduling electric vehicles with microgrids Zheng, Zedong; Yang, Shengxiang The explosion in the number of electric vehicles (EVs) has had a significant impact on the energy systems and structures of cities. Large-scale EVs inevitably increase the load on the grid, while uncoordinated vehicle to Grid (V2G) technologies pose challenges to the stability and security of the grid. This paper introduces a global intelligent method to find optimal cooperation charging/discharging strategies for EVs to minimize the operation cost. EVs aggregates co-ordinate the energy information and needs of all EVs and use real-time pricing based on micro-grid loads to influence EV charge-discharge behavior. Particle swarm optimization (PSO) is introduced to solve the EV scheduling problem. This study also discusses the negative impact on the energy system of different strategies for charging EVs. Simulation shows that this smart charging strategy and improved PSO can effectively decrease the operation cost of EVs and reduce the load for each micro-grid. The file attached to this record is the author's final peer reviewed version.
  • An experimental study of prediction methods in robust optimization over time
    An experimental study of prediction methods in robust optimization over time Fox, Matthew; Yang, Shengxiang; Caraffini, Fabio Robust Optimization Over Time (ROOT) is a new method of solving Dynamic Optimization Problems in respect to choosing a robust solution, that would last over a number of environment changes, rather than the approach that chooses the optimal solution at every change. ROOT methods currently show that ROOT can be solved by predicting an individual fitness for a number of future environment changes. In this work, a benchmark problem based on the Modified Moving Peaks Benchmark (MMPB) is proposed that includes an attractor heuristic, that guides optima to a determined location in the environment, resulting in a more predictable optimum. We study a number of time series forecasting methods to test different prediction methods of future fitness values in a ROOT method. Four time series regression techniques are considered as the prediction method: Linear and Quadratic Regression, an Autoregressive model, and Support Vector Regression. We find that there is not much difference in choosing a simple Linear Regression to more advanced prediction methods. We also suggest that current benchmark problems that cannot be predicted will deceive the optimizer and ROOT framework as the peaks may move using a random walk. Results show an improvement in comparison with MMPB used in most ROOT studies.
  • An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme
    An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme Guo, Jinglei; Shao, Miaomiao; Jiang, Shouyong; Yang, Shengxiang The multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem(MOP) into a number of single-objective subproblems. Penalty boundary intersection (PBI) in MOEA/D is one of the most popular decomposition approaches and has attracted significant attention. In this paper, we investigate two recent improvements on PBI, i.e. adaptive penalty scheme (APS) and subproblem-based penalty scheme (SPS), and demonstrate their strengths and weaknesses. Based on the observations, we further propose a hybrid penalty scheme (HPS), which adjusts the PBI penalty factor for each subproblem in two phases, to ensure the diversity of boundary solutions and good distribution of intermediate solutions. HPS specifies a distinct penalty value for each subproblem according to its weight vector. All the penalty values of subproblems increase with the same gradient during the first phase, and they are kept unchanged during the second phase.

Click here to view a full listing of Shengxiang Yang's publications and outputs.

Research interests/expertise

  • Evolutionary Computation

  • Swarm Intelligence

  • Meta-heuristics

  • Dynamic Optimisation Problems

  • Multi-objective Optimisation Problems

  • Relevant Real-World Applications

Areas of teaching

Research Methods for Intelligent Systems and Robotics MSc, Software Engineering MSc, Computing MSc, and Business Intelligence Systems and Data Mining MSc Degrees.

Qualifications

BSc in Automatic Control, Northeastern University, China (1993)

MSc in Automatic Control, Northeastern University, China (1996)

PhD in Systems Engineering Northeastern University, China (1999)

Courses taught

I have taught numerous modules at both undergraduate and postgraduate level. Quite a number of modules I taught were significantly developed by myself. The modules I taught are usually designed to be practice-oriented with problem-solving lab sessions based on Java or C++ programming, and hence are highly interesting to and greatly useful for students. They are also very important for different degree programmes in Computer Science and relevant subjects. Some of the modules I have taught are listed as follows:

  • CS3002 Artificial Intelligence (2010 – 2012, Brunel University): 3rd year Computer Science (Artificial Intelligence) BSc module, module leader

  • CS2005 Networks and Operating Systems (2010 – 2012, Brunel University): 2nd year Network Computing BSc module, part module

  • CS5518 Business Integration (2011-2012, Brunel University): Business Systems Integration MSc module, part module

  • CO2017 Networks and Distributed Systems (2005–2010, University of Leicester): 2nd year Computer Science BSc module, module leader

  • CO2005 Object-Oriented Programming Using C++ (2006–2009, University of Leicester): 2nd year Computer Science BSc module, module leader

  • CO1003 Program Design (2006-2007, University of Leicester): 1st year Computer Science BSc module, module leader

  • CO3097 Programming Secure and Distributed Systems (2003–2005, University of Leicester): 3rd year Computer Science BSc & Advanced Computer Science MSc module, module leader

  • CO1017 Operating Systems and Networks (2001 – 2004, University of Leicester): 1st year Computer Science BSc module, module leader

  • CO1016 Computer Systems (2000 – 2002, University of Leicester): 1st year Computer Science BSc module, part module

I have also co-ordinated several BSc projects, as shown below.

  • CS3072/CS3074/CS3105/CS3109 BSc Final Year Projects (2010 – 2012, Brunel University): Co-ordination Team Member

  • CO3012/CO3013/CO3015 Computer Science BSc Final Year Projects (2004 – 2010, University of Leicester): Co-ordinator

  • CO3120 Computer Science with Management BSc Final Year Project (2007 – 2010, University of Leicester): Co-ordinator

  • CO3014 Mathematics and Computer Science BSc Final Year Project (2004 – 2010, University of Leicester): Co-ordinator

  • CO2015 Second Year BSc Software Engineering Project (2003 – 2004, University of Leicester): Co-ordinator

Honours and awards

  • Nominatee to the Best Paper Award for EvoApplications 2016: Applications of Evolutionary Computation, for the paper "Direct memory schemes for population-based incremental learning in cyclically changing environments" by Michalis Mavrovouniotis and Shengxiang Yang, published in EvoApplications 2016: Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol. 9598, pp. 233-247, 2016.

  • Nominatee for the Best-Paper Award of the ACO-SI Track at the 2015 Genetic and Evolutionary Computation Conference, for the paper "An ant colony optimization based memetic algorithm for the dynamic travelling salesman problem" by Michalis Mavrovouniotis, Felipe Martins Muller and Shengxiang Yang, published in the Proceedings of the 17th Annual Conference on Genetic and Evolutionary Computation, pp. 49-56, 2015.

  • Winner of the 2014 IEEE Congress on Evolutionary Computation Best Student Paper Award, for the paper entitled "A test problem for visual investigation of high-dimensional multi-objective search" by Miqing Li, Shengxiang Yang and Xiaohui Liu, published in the Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 2140-2147, 2014.

  • Nominatee for the 2005 Genetic and Evolutionary Computation Conference Best Paper Award, for the paper "Memory-based immigrants for genetic algorithms in dynamic environments" by Shengxiang Yang, published in the Proceedings of the 2005 Genetic and Evolutionary Computation Conference, Vol. 2, pp. 1115-1122, 2005.

  • Visiting Professor (2012 – 2014, 2016-2018), College of Information Engineering, Xiangtan University, China

  • Visiting Professor (2011 – 2017), College of Mathematics and Statistics, Nanjing University of Information Science and Technology, China

Membership of professional associations and societies

  • Founding Chair, Task Force on Intelligent Network Systems (TF-INS), Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (IEEE CIS), 2012–2018.

  • Chair, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), 2011–2018.

  • Senior Member, IEEE, since 2014.

  • Member, IEEE, 2000 – 2013.

  • Member, IEEE Computational Intelligence Society (IEEE CIS), since 2005.

  • Member, Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), since 2011.

  • Member, Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (IEEE CIS), since 2013.

  • Member, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), 2003 – 2010.

Current research students

First Supervisor:

  • Muhanad Tahrir Younis: Swarm intelligence for dynamic job scheduling in grid computing, started from October 2014

  • Conor Fahy: Evolutionary computation for data stream analysis, started from October 2015

  • Zedong Zheng: started from October 2016
  • Matthew Fox: started from October 2017

Second Supervisor:

  • Ahad Arshad: PhD candidate, co-supervised with Prof. Paul Fleming at De Montfort University, started in October 2017.
  • William Lawrence: PhD candidate, co-supervised with Dr. Mario Gongora at De Montfort University, started in April 2012

Complete PhD Students (I was the 1st Supervisor):

  • Changhe Li: Particle swarm optimisation in stationary and dynamic environments, 2011

  • Imtiaz Ali Korejo: Adaptive mutation operators for evolutionary algorithms, 2011

  • Sadaf Naseem Jat: Genetic algorithms for university course timetabling problems, 2012

  • Shakeel Arshad: Sequence based memetic algorithms for static and dynamic travelling salesman problems, 2012

  • Michalis Mavrovouniotis: Ant Colony Optimization in Stationary and Dynamic Environments, 2013

  •  Miqing Li: Evolutionary Many-Objective Optimization: Pushing the Boundaries, 2015
  • Jayne Eaton: Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems, 2017
  • Shouyong Jiang: Evolutionary Algorithms for Static and Dynamic Multiobjective Optimization, 2017

Externally funded research grants information

  • EU Horizon 2020 Marie Sklodowska-Curie Individual Fellowships (PI, Project ID: 661327, 09/2015-08/2017, €195,455): Evolutionary Computation for Dynamic Constrained Optimization Problems (ECDCOP)
  • EPSRC (PI, Standard Research Project, EP/K001310/1, 18/2/2013-17/02/2017, £445,069): Evolutionary Computation for Dynamic Optimisation in Network Environments

  • EPSRC (PI, Standard Research Project, EP/E060722/1 and EP/E060722/2, 1/1/2008-1/7/2011, £307,469): Evolutionary Algorithms for Dynamic Optimisation Problems: Design, Analysis and Applications

  • EPSRC (PI, Overseas Travel Grants GR/S79718/01, 1/11/2003-31/1/2004, £6,700): Adaptive and Hybrid Genetic Algorithms for Production Scheduling Problems in Manufacturing. This grant supported my research visit to Waseda University, Japan, during my Sabbatical leave period. Additionally, Waseda University, Japan contributed JPY140,000 (~£800) toward the visit

  • State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, China (PI, Open Research Project, 1/1/2012-31/12/2013, CNY300,000 (~£30,000)): Evolutionary Computation for Dynamic Scheduling Problems in Process Industries

  • State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, China (PI, Open Research Project, 1/1/2010-31/12/2011, CNY150,000 (~£15,000)): Evolutionary Computation for Dynamic Optimization and Scheduling Problems

  • Transport iNet, European Regional Development Fund (Co-I, 11/11/2013 - 28/02/2015, £62,134), Evolutionary Computation for Optimised Rail Travel (EsCORT). This is a linked project between De Montfort University and Go Travel Solutions, a Leicester based SME specialising in assisting businesses to develop sustainable travel solutions, covering people and goods.
  • Hong Kong Polytechnic University Research Grants (Co-I, Grant G-YH60, 1/7/2009-30/6/2010, HKD120,000 (~£10,000)): Improved Evolutionary Algorithms with Primal-Dual Population for Dynamic Variation in Production Systems. Partners:

In addition, I have also received several conference travel grants from UK Research Councils, e.g., Royal Society Conference Travel Grant (£700 in 2007 and £719 in 2005) and Royal Academy of Engineering Conference Grant (£800 in 2007 and £1,200 in 2006).

Internally funded research project information

  • De Montfort University Higher Education Innovation Fund (HEIF) 2017-18 (Co-I, 01/12/2017-31/07/2018, £14,000): Brian-Computer-Interface Prototyping System: Data-based Filtering and Dynamic Characterisation.
  • De Montfort University Higher Education Innovation Fund (HEIF) 2015-16 (PI, 01/01/2016-31/07/2016, £24,800): Development of a Dynamic Resource Scheduling Prototype System for Airports.

  • De Montfort University PhD Studentships 2017-18 (PI, 1/10/2017–30/09/2020, approximately £60,000): supporting stipend and fees for one EU/Home PhD student for three years

  • De Montfort University Fee Waiver PhD Scholarships 2016-17 (PI, 1/10/2016–30/09/2019, approximately £40,000): supporting fees for one overseas PhD student for three years

  • De Montfort University PhD Studentships 2015-16 (PI, 1/10/2015–30/09/2018, approximately £60,000): supporting stipend and fees for one EU/Home PhD student for three years

  • De Montfort University PhD Studentships 2013-14 (PI, 1/10/2013–30/09/2016, approximately £80,000): supporting stipend and fees for one overseas PhD student for three years

  • De Montfort University PhD Studentships 2013-14 (PI, 1/4/2013–31/03/2016, approximately £60,000): supporting stipend and fees for one home PhD student for three years

  • Brunel University PhD Studentships 2011-12 (PI, 01/10/2011–30/09/2014, approximately £80,000): supporting stipend and fees for one overseas PhD student for three years

  • University of Leicester PhD Studentships 2008-09 (PI, 1/10/2008–30/9/2011, approximately £50,000): supporting stipend and fees for one PhD student for three years

  • University of Leicester Research Fund 2001 (PI, 1/1/2001- 31/12/2001, £3,200): Using Neural Network and Genetic Algorithm Methods for Job-Shop Scheduling Problem.

Professional esteem indicators

Shengxiang-Yang

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