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Prediction of recurrent venous thromboembolism using a spatiotemporal phenomenological model and artificial neural network

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Neural Computing and Applications 2025年第15期37卷 9193-9221页

作者： Al Bannoud, Mohamad Martins, Tiago Dias de Lima Montalvão, Silmara Aparecida Annichino-Bizzacchi, Joyce Maria Filho, Rubens Maciel Maciel, Maria Regina Wolf School of Chemical Engineering Laboratory of Optimization Design and Advanced Control Universidade Estadual de Campinas São Paulo Campinas Brazil Departamento de Engenharia Química Instituto de Ciências Ambientais Químicas e Farmacêuticas Universidade Federal de São Paulo São Paulo Diadema Brazil Hematology and Hemotherapy Center University of Campinas/Hemocentro-Unicamp Instituto Nacional de Ciência E Tecnologia Do Sangue São Paulo Campinas Brazil Universidade Estadual de Campinas São Paulo Campinas Brazil

Venous thromboembolism (VTE) is a major global cause of death, with recurrence risk rising after inadequate treatment. Existing VTE recurrence prediction models often overlook the phenomenological aspects of blood clot formation. This study aimed to predict Recurrent VTE (RVTE) by combining an artificial neural network (ANN) with a phenomenological clot formation model. A spatiotemporal model of clot formation was proposed and simulated using hematological data from 235 patients. Principal component analysis (PCA) and partial least squares (PLS) regression were applied to reduce the number of significant variables, and multiple ANN structures were trained for RVTE predictions. The dataset was initially divided into training and validation subsets to avoid data leakage. No data from the validation subset was used to build the model, ensuring the model's integrity. The validation conducted in this study corroborates the model's good generalizability. The spatiotemporal model effectively differentiated patients based on clot formation characteristics. The application of PCA and PLS reduced the original variables by over 99.99%. The best-performing model achieved, for unseen observations, an area under curve of 0.9689, accuracy of 0.9149, sensitivity of 1.0000, specificity of 0.8919, and F1-score of 0.8333, aligning with established literature values. This methodology outperformed nine other machine learning models commonly used for binary classification. A significant advantage of this approach is its reliance on only six clinical variables for prediction. This study demonstrates that mathematical methods can reveal insights into the spatiotemporal evolution of blood clots, presenting a promising avenue for RVTE prediction. The model could serve as a clinical decision-support system for RVTE treatment. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Prediction models

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Scatter Search Based Particle Swarm optimization Algorithm for Earliness/Tardiness Flowshop Scheduling with Uncertainty

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International Journal of Automation and computing 2016年第3期13卷 285-295页

作者： Jia-Can Geng Zhe Cui Xing-Sheng Gu Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and Technology

Considering the imprecise nature of the data in real-world problems, the earliness/tardiness （E/T） fiowshop scheduling problem with uncertain processing time and distinct due windows is concerned in this paper. A fuzzy scheduling model is established and then transformed into a deterministic one by employing the method of maximizing the membership function of middle value. Moreover, an effective scatter search based particle swarm optimization （SSPSO） algorithm is proposed to minimize the sum of total earliness and tardiness penalties. The proposed SSPSO algorithm incorporates the scatter search （SS） algorithm into the frame of particle swarm optimization （PSO） algorithm and gives full play to their characteristics of fast convergence and high diversity. Besides, a differential evolution （DE） scheme is used to generate solutions in the SS. In addition, the dynamic update strategy and critical conditions are adopted to improve the performance of SSPSO. The simulation results indicate the superiority of SSPSO in terms of effectiveness and efficiency.

关键词： Earliness/tardiness （E/T） scheduling fuzzy modeling scatter search （SS） particle swarm optimization （PSO）.

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Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection

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Frontiers of Chemical Science and Engineering 2017年第3期11卷 429-439页

作者： Xin Peng Yang Tang Wenli Du Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai 200237 China

In this paper, we propose a novel performance monitoring and fault detection method, which is based on modified structure analysis and globality and locality preserving （MSAGL） projection, for non-Gaussian processes with multiple operation conditions. By using locality preserving projection to analyze the embedding geometrical manifold and extracting the non-Gaussian features by independent component analysis, MSAGL preserves both the global and local structures of the data simultaneously. Furthermore, the tradeoff parameter of MSAGL is tuned adaptively in order to find the projection direction optimal for revealing the hidden structural information. The validity and effectiveness of this approach are illustrated by applying the proposed technique to the Tennessee Eastman process simulation under multiple operation conditions. The results demonstrate the advantages of the proposed method over conventional eigendecomposition-based monitoring methotis.

关键词： non-Gaussian processes subspace projection independent component analysis locality preserving projection finite mixture model

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Wind field reconstruction for the dispersion modeling of accidental chemical spills on complex geometry

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Chinese Journal of Chemical Engineering 2019年第11期27卷 2712-2724页

作者： Bing Wang Feng Qian Weimin Zhong Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Chemical spills on complex geometry are difficult to model due to the uneven concentration distribution caused by air flow over ground obstacles. Computational fluid dynamics(CFD) is one of the powerful tools to estimate the building-resolving wind flow as well as pollutant dispersion. However, it takes too much time and requires enormous computational power in emergency situations. As a time demanding task, the estimation of the chemical spill consequence for emergency response requires abundant wind field information. In this paper, a comprehensive wind field reconstruction framework is proposed, providing the ability of parameter tuning for best reconstruction accuracy. The core of the framework is a data regression model built on principal component analysis(PCA) and extreme learning machine(ELM). To improve the accuracy, the wind field estimation from the regression model is further revised from local wind observations. The optimal placement of anemometers is provided based on the maximum projection on minimum eigenspace(MPME) algorithm. The fire dynamic simulator(FDS) generates high-resolution data of wind flow over complex geometries for the framework to be implemented. The reconstructed wind field is evaluated against simulation data and an overall reconstruction error of 9% is achieved. When used in real case,the error increases to around 12% since no convergence check is available. With parameter tuning abilities,the proposed framework provides an efficient way of reconstructing the wind flow in congested areas.

关键词： Wind field reconstruction CFD PCA Extreme learning machine Sensor placement

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A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

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Journal of Central South University 2017年第8期24卷 1779-1788页

作者：孙泽文顾幸生 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education(East China University of Science and Technology)Shanghai 200237China

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

关键词： hybrid estimation of distribution algorithm teaching learning based optimization strategy hybrid flow shop unrelated parallel machine scheduling

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Monocular depth estimation based on deep learning: An overview

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Science China(Technological Sciences) 2020年第9期63卷 1612-1627页

作者： ZHAO ChaoQiang SUN QiYu ZHANG ChongZhen TANG Yang QIAN Feng Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Depth information is important for autonomous systems to perceive environments and estimate their own state. Traditional depth estimation methods, like structure from motion and stereo vision matching, are built on feature correspondences of multiple viewpoints. Meanwhile, the predicted depth maps are sparse. Inferring depth information from a single image(monocular depth estimation) is an ill-posed problem. With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy. Meanwhile, dense depth maps are estimated from single images by deep neural networks in an end-to-end manner. In order to improve the accuracy of depth estimation, different kinds of network frameworks, loss functions and training strategies are proposed subsequently. Therefore, we survey the current monocular depth estimation methods based on deep learning in this review. Initially, we conclude several widely used datasets and evaluation indicators in deep learning-based depth estimation. Furthermore, we review some representative existing methods according to different training manners: supervised, unsupervised and semi-supervised. Finally, we discuss the challenges and provide some ideas for future researches in monocular depth estimation.

关键词： autonomous systems monocular depth estimation deep learning unsupervised learning

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Multi-objective modeling and optimization for scheduling of cracking furnace systems

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Chinese Journal of Chemical Engineering 2017年第8期25卷 992-999页

作者： Peng Jiang Wenli Du Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education School of Information Science and Engineering East China University of Science and Technology

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.

关键词： Cracking furnace systems Feed scheduling Multi-objective mixed integer nonlinear optimization Genetic algorithm

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Combined Timetabling Procedure and Complete Local Search for No-Wait Job Shop Scheduling with Total Tardiness

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Journal of Donghua University(English Edition) 2014年第2期31卷 83-91页

作者：杨玉珍顾幸生 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and Technology

The strong non-deterministic polynomial-hard( NP-hard)character of job shop scheduling problem( JSSP) has been acknowledged widely and it becomes stronger when attaches the nowait constraint,which widely exists in many production processes,such as chemistry process, metallurgical process. However,compared with the massive research on traditional job shop problem,little attention has been paid on the no-wait ***,in this paper, we have dealt with this problem by decomposing it into two sub-problems, the timetabling and sequencing problems,in traditional frame work. A new efficient combined non-order timetabling method,coordinated with objective of total tardiness,is proposed for the timetabling problems. As for the sequencing one,we have presented a modified complete local search with memory combined by crossover operator and distance counting. The entire algorithm was tested on well-known benchmark problems and compared with several existing *** experiments showed that our proposed algorithm performed both effectively and efficiently.

关键词： job shop scheduling no-wait timetabling tardiness complete local search with memory

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Hybrid Discrete Harmony Search Algorithm for Flow Shop Scheduling with Limited Buffers

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Journal of Donghua University(English Edition) 2014年第2期31卷 171-178页

作者：崔喆顾幸生 Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Scienceand Technology

The flow shop scheduling problem with limited buffers( LBFSP) widely exists in manufacturing systems. A hybrid discrete harmony search algorithm is proposed for the problem to minimize total flow time. The algorithm presents a novel discrete improvisation and a differential evolution scheme with the jobpermutation-based representation. Moreover,the discrete harmony search is hybridized with the problem-dependent local search based on insert neighborhood to balance the global exploration and local exploitation. In addition, an orthogonal experiment design is employed to provide a receipt for turning the adjustable parameters of the algorithm. Comparisons based on the Taillard benchmarks indicate the superiority of the proposed algorithm in terms of effectiveness and efficiency.

关键词： multiproduct processes scheduling problem limited buffers total flow time harmony search

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An effective discrete artificial bee colony algorithm for flow shop scheduling problem with intermediate buffers

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Journal of Central South University 2015年第9期22卷 3471-3484页

作者：张素君顾幸生 School of Mechanical and Electrical Engineering Henan Institute of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology

An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value.

关键词： discrete artificial bee colony algorithm flow shop scheduling problem with intermediate buffers destruction and construction tournament selection

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