Article
Computer Science, Artificial Intelligence
Hideaki Ishibashi, Shotaro Akaho
Summary: This letter proposes an extension of principal component analysis for gaussian process (GP) posteriors, denoted by GP-PCA. It introduces a low-dimensional space estimation for GP posteriors, which can be used for metalearning to enhance the performance of target tasks. The study addresses the challenge of defining a structure for a set of GPs with infinite-dimensional parameters by reducing the infinite dimensionality to finite-dimensional case using information geometrical framework. Additionally, an approximation method based on variational inference is proposed and the effectiveness of GP-PCA as meta-learning is demonstrated through experiments.
NEURAL COMPUTATION
(2022)
Article
Statistics & Probability
Guanying Qi, Min-Qian Liu, Jian-Feng Yang
Summary: This paper discusses the growing trend of using multiple computer codes with different levels of accuracy in engineering and science to study complex systems. It proposes an integral model that combines simulation results obtained at different levels of accuracy to produce better predictions. The effectiveness of the proposed model is demonstrated through several examples.
Article
Mathematics
Yaohui Li, Junjun Shi, Zhifeng Yin, Jingfang Shen, Yizhong Wu, Shuting Wang
Summary: The proposed high-dimensional Kriging modeling method through principal component dimension reduction (HDKM-PCDR) can achieve faster modeling efficiency while reducing time consumption.
Article
Engineering, Chemical
Zhijiang Lou, Youqing Wang, Shan Lu, Pei Sun
Summary: This study proposes a novel robust PCA scheme called MRPCA, which adopts a difference selection mechanism for outlier samples in the offline training stage and an outlier detection mechanism for distinguishing outliers from fault data in the online monitoring stage. With these mechanisms, MRPCA achieves high fault detection rates and low false alarm rates in tests.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2021)
Article
Chemistry, Multidisciplinary
Lucas Kwai Hong Lui, C. K. M. Lee
Summary: This research investigated a mathematical model of earphone design with principal component analysis and formulated a predictive model for sound quality indicators. The study simplified the design problem and utilized principal component analysis to decrease the number of input variables. The results showed suboptimal predictive accuracy for the sound quality indicators but obtained a simplified formulation.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Industrial
Yushan Liu, Luyi Li, Zeming Chang
Summary: This paper proposes a novel Bayesian updating framework based on principal component analysis (PCA) to solve the challenging problem of updating dynamic systems with high-dimensional output. The framework constructs a new likelihood function based on low-dimensional output principal components (PCs), which has been analytically proven to provide equivalent likelihood measures to the original one. An efficient Bayesian updating algorithm is also proposed in the PCA-based framework, which incorporates adaptive Bayesian updating with structural reliability methods (aBUS) and the Kriging model. Four examples are conducted to validate the proposed method.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Chemical
Yuan Li, Dongsheng Yang
Summary: LCPCA is a novel approach for monitoring the status of multimode processes without the need for prior knowledge, by dividing data into local components and applying posterior probability. It outperforms conventional PCA and LNS-PCA in fault detection rate based on numerical examples and the TE process.
CHINESE JOURNAL OF CHEMICAL ENGINEERING
(2021)
Article
Mechanics
Zhibao Cheng, Min Li, Gaofeng Jia, Zhifei Shi
Summary: This paper proposes an adaptive Gaussian process (AGP) model to efficiently predict the complex dispersion relations for periodic structures. It first predicts the coefficients of the dispersion equation at selected frequencies, and then analytically solves the dispersion equation to establish the complex dispersion relation. PCA is used to reduce the dimension of these coefficients, and an adaptive procedure is integrated to improve the accuracy of the GP model.
EUROPEAN JOURNAL OF MECHANICS A-SOLIDS
(2022)
Article
Computer Science, Interdisciplinary Applications
Casey B. Davis, Christopher M. Hans, Thomas J. Santner
Summary: This paper introduces a new Bayesian global-trend plus local-trend model extension that allows for measurement errors and introduces a weight function to allocate total process variability. It ensures that the fitted global mean is smoother than local deviations and provides a flexible mechanism for handling variance functions that vary across the input space using a Gaussian process for the log of the process variance. The method is illustrated using both analytic and real-data examples.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Statistics & Probability
Yunfei Wei, Daijun Chen, Shifeng Xiong
Summary: This paper investigates projection pursuit emulation for computer experiments with multiple input variables. The method aims to capture the most influential input directions to reduce active dimensionality. Its interpolation property is proven under specific conditions, and a two-stage method is proposed to handle situations where the projection pursuit method fails to converge. Simulation studies demonstrate that the proposed methods are more efficient than traditional Kriging methods.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2022)
Article
Statistics & Probability
Annie Sauer, Robert B. Gramacy, David Higdon
Summary: This study explores the use of Deep Gaussian Processes (DGPs) as surrogate models for computer simulation experiments, utilizing novel Bayesian inference methods to quantify input space warping and uncertainty. This allows for nonuniform distribution of runs in the input space, improving computational efficiency and reducing evaluation costs of the simulator code.
Article
Statistics & Probability
Elham Yousefi, Luc Pronzato, Markus Hainy, Werner G. Mueller, Henry P. Wynn
Summary: This paper discusses the design and analysis of experiments to distinguish between Gaussian process models with different covariance kernels. Two frameworks are explored: sequential constructions and static criteria. The selection of observation points in sequential constructions is based on the maximization of the difference between symmetric Kullback Leibler divergences or the mean squared error of the models. Static criteria include log-likelihood ratios, Frechet distance, and other distance-based criteria. The paper also examines the mathematical links between different criteria and provides numerical illustrations.
STATISTICAL PAPERS
(2023)
Article
Chemistry, Physical
Homero Valladares, Tianyi Li, Likun Zhu, Hazim El-Mounayri, Ahmed M. Hashem, Ashraf E. Abdel-Ghany, Andres Tovar
Summary: This study utilizes Gaussian processes (GPs) to predict and optimize the performance of lithium-ion batteries (LIBs) through co-kriging surrogate modeling and Bayesian optimization. The results show that co-kriging surrogate can accurately predict the capacity degradation profile of the battery, while Bayesian optimization can identify new Ni compositions with high initial specific capacity and large cycle life.
JOURNAL OF POWER SOURCES
(2022)
Article
Statistics & Probability
Remi Stroh, Julien Bect, Severine Demeyer, Nicolas Fischer, Damien Marquis, Emmanuel Vazquez
Summary: This article discusses the sequential design of experiments for multi-fidelity numerical simulators, proposing a new Bayesian strategy called maximal rate of stepwise uncertainty reduction (MR-SUR) which aims to maximize the ratio between expected reduction of uncertainty and simulation cost. The strategy unifies existing methods and provides a principled approach for developing new ones.
Article
Chemistry, Analytical
Shayaan Saghir, Muhammad Mubasher Saleem, Amir Hamza, Kashif Riaz, Sohail Iqbal, Rana Iqtidar Shakoor
Summary: This study presents a systematic and efficient design approach for optimizing multiple output responses of a 2-DoF capacitive MEMS accelerometer using DACE and GP modeling. Metamodels for individual output responses were developed allowing analysis of design parameters and interactions. The design methodology was validated using FEM simulations and shown to be effective for design space exploration and optimization of multiphysics MEMS devices in the development cycle.
Article
Materials Science, Composites
Zhongwei Chen, Yifan Suo, Yuan Yu, Tingting Chen, Changxin Li, Qingwu Zhang, Juncheng Jiang, Tao Chen
Summary: Graphitic carbon nitride (GCN) is considered a potential flame retardant due to its high stability and nitrogen content. A covalent modification approach was developed in this study to combine GCN with metal ions, resulting in uniform dispersion in epoxy resin. Fire tests demonstrated that the modified epoxy resin exhibited excellent flame retardant properties, with increased oxygen index and reduced heat release, smoke production, and harmful gas emissions.
COMPOSITES COMMUNICATIONS
(2022)
Article
Agricultural Engineering
Hui Li, Stavros Chatzifotis, Guoping Lian, Yanqing Duan, Daoliang Li, Tao Chen
Summary: The study aims to develop a mechanistic model based optimization method to determine aquaculture feeding programs. By integrating a fish weight prediction model and a requirement analysis model, balanced and sustainable feed formulations and effective feeding programs can be designed. The simulation results demonstrate that this approach can significantly improve aquaculture production.
AQUACULTURAL ENGINEERING
(2022)
Article
Multidisciplinary Sciences
Faiza Benaouda, Ricardo Inacio, Chui Hua Lim, Haeeun Park, Thomas Pitcher, Mohamed A. Alhnan, Mazen M. S. Aly, Khuloud Al-Jamal, Ka-lung Chan, Rikhav P. Gala, Daniel Sebastia-Saez, Liang Cui, Tao Chen, Julie Keeble, Stuart A. Jones
Summary: This study demonstrates a novel method for needleless delivery of advanced therapies using a skin patch. By opening the skin appendages with a hypobaric chamber, direct delivery of vaccine antigens and drug nanoparticles was achieved. The patch was shown to enhance immune response to vaccine antigens and effectively reduce rat paw swelling.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Review
Pharmacology & Pharmacy
David O. Oluwole, Lucy Coleman, William Buchanan, Tao Chen, Roberto M. La Ragione, Lian X. Liu
Summary: The rapid rise in the health burden of chronic wounds has led to great concern among policymakers, academia, and industry. Antibiotics are not always effective in treating chronic wounds due to poor penetration of bacterial biofilms and antimicrobial resistance. This review focuses on non-antibiotic compounds with proven or potential antimicrobial, anti-inflammatory, antioxidant, and wound healing efficacy, including Aloe vera, curcumin, cinnamaldehyde, polyhexanide, retinoids, ascorbate, tocochromanols, and chitosan. These compounds could provide a reliable alternative for the management or prevention of chronic wounds.
Article
Engineering, Multidisciplinary
Zhongwei Chen, Yong Guo, Yanpeng Chu, Tingting Chen, Qingwu Zhang, Changxin Li, Juncheng Jiang, Tao Chen, Yuan Yu, Lianxiang Liu
Summary: A solvent-free mechanochemical method was used to prepare phosphorus-nitrogen flame retardants (PNFRs), which effectively reduced the fire hazard of epoxy resin without affecting its mechanical and thermal properties.
COMPOSITES PART B-ENGINEERING
(2022)
Article
Thermodynamics
Xueliang Zhu, Xuhai Pan, Yu Mei, Jiajia Ma, Hao Tang, Yucheng Zhu, Lian X. Liu, Juncheng Jiang, Tao Chen
Summary: Superheated liquid jets disintegrate into droplets due to thermal nonequilibrium induced flashing and mechanical forces during depressurization. This study investigates the breakup and droplet formation of superheated liquid jets under depressurized releases using an experimental tank. The interaction between thermodynamic and mechanical effects is discussed and a quantitative relationship is established. The results show different breakup modes and highlight the importance of considering the cooling effect in addition to thermodynamic and mechanical effects.
APPLIED THERMAL ENGINEERING
(2023)
Article
Toxicology
Marina V. Evans, Thomas E. Moxon, Guoping Lian, Benjamin N. Deacon, Tao Chen, Linda D. Adams, Annabel Meade, John F. Wambaugh
Summary: This study investigates the applicability of the Potts-Guy equation in different human skin datasets and proposes an updated regression equation that combines mechanistic and structural activity relationships. The results show that the Potts-Guy equation is more applicable to experiments focused on the epidermis, while it performs poorly for dermatomed skin and full skin. The combination approach results in improved regression fit compared to the Potts-Guy approach alone.
JOURNAL OF APPLIED TOXICOLOGY
(2023)
Article
Chemistry, Multidisciplinary
Daniel Sebastia-Saez, Faiza Benaouda, Chui Hua Lim, Guoping Lian, Stuart A. Jones, Liang Cui, Tao Chen
Summary: This study developed a simulation model to explore the interaction between mechanical stretch and diffusion of large molecules in the skin under hypobaric pressure. The results showed that hypobaric pressure increased diffusion in the skin, leading to improved transdermal permeation.
PHARMACEUTICAL RESEARCH
(2023)
Article
Engineering, Environmental
Xueliang Zhu, Xuhai Pan, Hao Tang, Xilin Wang, Yucheng Zhu, Lian X. Liu, Juncheng Jiang, Tao Chen
Summary: In this study, a 20 L tank was used to investigate the two-phase flow behaviors during depressurized releases of superheated liquids. The researchers utilized high-speed camera and phase Doppler anemometry to analyze the characteristics of the jet and its breakup. Based on the interaction between thermodynamic and mechanical effects, quantitative criteria were developed to distinguish different breakup regimes.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2023)
Article
Thermodynamics
Xueliang Zhu, Xuhai Pan, Jiajia Ma, Yu Mei, Hao Tang, Yucheng Zhu, Lianxiang Liu, Juncheng Jiang, Tao Chen
Summary: This study investigates the dynamic behavior of superheated liquids and their impact on flashing jet during accidental releases. The results show a strong correlation between the depressurization and the initial ηp0, and flashing occurs upstream and chokes the two-phase flow, reducing the release rate. These findings provide insights for the prevention and mitigation of accidents involving superheated liquid releases.
INTERNATIONAL JOURNAL OF THERMAL SCIENCES
(2023)
Article
Pharmacology & Pharmacy
Lucy Coleman, James R. G. Adams, Will Buchanan, Tao Chen, Roberto M. M. La Ragione, Lian X. X. Liu
Summary: Chronic wounds are a significant burden to patients and healthcare systems, complicated by bacterial infection. This study screened several non-antibiotic compounds for their antibacterial and antibiofilm capabilities and found that PHMB exhibited highly effective antibacterial activity, while TPGS demonstrated potent antibiofilm properties. The combination of these two compounds resulted in a synergistic enhancement of their capability to kill bacteria and disperse biofilms. In conclusion, this work highlights the importance of combinatory approaches to treat infected chronic wounds where bacterial colonization and biofilm formation are significant issues.
Article
Spectroscopy
Anukrati Goel, Dimitrios Tsikritsis, Natalie A. Belsey, Ruth Pendlington, Stephen Glavin, Tao Chen
Summary: Stimulated Raman scattering microscopy is a label-free chemical imaging tool that can map the distribution of chemicals in the skin. This study combines SRS measurements with chemometrics to quantify the penetration profile of exogenous substances in human skin. It is the first demonstration of using SRS imaging technique with spectral unmixing methods for direct observation and mapping of chemical penetration and distribution in biological tissues.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2023)
Article
Environmental Sciences
Jinqi Yang, Yu Guo, Tao Chen, Lang Qiao, Yang Wang
Summary: This study proposed an Adaptive Time Pattern Network (ATPNet) to predict the air temperature of a greenhouse aquaponics system. The ATPNet improved the prediction performance by utilizing deep temporal features, multiple temporal patterns convolution, and a spatial attention mechanism. The ATPNet found a strong correlation between the air temperature of the system and other temperature-related parameters, leading to significant improvements in prediction accuracy compared to baseline models.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Engineering, Environmental
Zhongwei Chen, Boran Yang, Nannan Song, Tingting Chen, Qingwu Zhang, Changxin Li, Juncheng Jiang, Tao Chen, Yuan Yu, Lian X. Liu
Summary: By using machine learning, a high-performance EP composite with enhanced fire resistance was developed by incorporating OPFRs. The ML model identified fire retardants with specific molecular structures that significantly increased the LOI of EPs. Experimental validation confirmed the accuracy and reliability of the ML model.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Toxicology
Benjamin N. Deacon, Nicola Piasentin, Qiong Cai, Tao Chen, Guoping Lian
Summary: Permeability and partition coefficients of the skin barrier are important for evaluating the absorption and safety of cosmetics and medicine. This study analyzes the relationship between skin permeability and hydrophobicity using the Potts and Guy equation and identifies significant differences in published datasets. The weak correlation and dependence of the late permeability datasets with skin lipid/water partition suggest the presence of additional non-lipid pathways or a weaker skin barrier property.
TOXICOLOGY IN VITRO
(2023)
Article
Computer Science, Interdisciplinary Applications
Nohan Joemon, Melpakkam Pradeep, Lokesh K. Rajulapati, Raghunathan Rengaswamy
Summary: This paper introduces a smoothing-based approach for discovering partial differential equations from noisy measurements. The method is data-driven and improves performance by incorporating first principles knowledge. The effectiveness of the algorithm is demonstrated in a real system using a new benchmark metric.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhibin Lu, Yimeng Li, Chang He, Jingzheng Ren, Haoshui Yu, Bingjian Zhang, Qinglin Chen
Summary: This study proposes a new inverse design method using a physics-informed neural network to identify optimal heat sink designs. A hybrid PINN accurately approximates the governing equations of heat transfer processes, and a surrogate model is constructed for integration with optimization algorithms. The proposed method accelerates the search for Pareto-optimal designs and reduces search time. Comparing different scenarios facilitates real-time observation of multiphysics field changes, improving understanding of optimal designs.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Luca Gasparini, Antonio Benedetti, Giulia Marchese, Connor Gallagher, Pierantonio Facco, Massimiliano Barolo
Summary: In this paper, a method for batch process monitoring with limited historical data is investigated. The methodology utilizes machine learning algorithms to generate virtual data and combines it with real data to build a process monitoring model. Automatic procedures are developed to optimize parameters, and indicators and metrics are proposed to assist virtual data generation activities.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Julia Jimenez-Romero, Adisa Azapagic, Robin Smith
Summary: Energy transition is a significant and complex challenge for the industry, and developing cost-effective solutions for synthesizing utility systems is crucial. The research combines mathematical formulation with realistic configurations and conditions to represent utility systems and provides a basis for synthesizing energy-efficient utility systems for the future.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Samuel Adeyemo, Debangsu Bhattacharyya
Summary: This work develops algorithms for estimating sparse interpretable data-driven models. The algorithms select the optimal basis functions and estimate the model parameters using Bayesian inferencing. The algorithms estimate the noise characteristics and model parameters simultaneously. The algorithms also exploit prior analysis and special properties for efficient pruning, and use a modified Akaike information criterion for model selection.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Abbasali Jafari-Nodoushan, Mohammad Hossein Dehghani Sadrabadi, Maryam Nili, Ahmad Makui, Rouzbeh Ghousi
Summary: This study presents a three-objective model to design a forward supply chain network considering interrelated operational and disruptive risks. Several strategies are implemented to cope with these risks, and a joint pricing strategy is used to enhance the profitability of the supply chain. The results show that managing risks and uncertainties simultaneously can improve sustainability goals and reduce associated costs.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
T. A. Espaas, V. S. Vassiliadis
Summary: This paper extends the concept of higher-order search directions in interior point methods to convex nonlinear programming. It provides the mathematical framework for computing higher-order derivatives and highlights simplified computation for special cases. The paper also introduces a dimensional lifting procedure for transforming general nonlinear problems into more efficient forms and describes the algorithmic development required to employ these higher-order search directions.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
David A. Linan, Gabriel Contreras-Zarazua, Eduardo Sanhez-Ramirez, Juan Gabriel Segovia-Hernandez, Luis A. Ricardez-Sandoval
Summary: This study proposes a parallel hybrid algorithm for optimal design of process flowsheets, which combines stochastic method with deterministic algorithm to achieve faster and improved convergence.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaoyong Lin, Zihui Li, Yongming Han, Zhiwei Chen, Zhiqiang Geng
Summary: A novel GAT-LSTM model is proposed for the production prediction and energy structure optimization of propylene production processes. It outperforms other models and can provide the optimal raw material scheme for actual production processes.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Prodromos Daoutidis, Jay H. Lee, Srinivas Rangarajan, Leo Chiang, Bhushan Gopaluni, Artur M. Schweidtmann, Iiro Harjunkoski, Mehmet Mercangoz, Ali Mesbah, Fani Boukouvala, Fernando Lima, Antonio del Rio Chanona, Christos Georgakis
Summary: This paper provides a concise perspective on the potential of machine learning in the PSE domain, based on discussions and talks during the FIPSE 5 conference. It highlights the need for domain-specific techniques in molecular/material design, data analytics, optimization, and control.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hesam Hassanpour, Prashant Mhaskar, Brandon Corbett
Summary: This work addresses the problem of designing an offset-free implementable reinforcement learning (RL) controller for nonlinear processes. A pre-training strategy is proposed to provide a secure platform for online implementations of the RL controller. The efficacy of the proposed approach is demonstrated through simulations on a chemical reactor example.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hunggi Lee, Donghyeon Lee, Jaewook Lee, Dongil Shin
Summary: This study introduces an innovative framework that utilizes a limited number of sensors to detect chemical leaks early, mitigating the risk of major industrial disasters, and providing faster and higher-resolution results.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Sibel Uygun Batgi, Ibrahim Dincer
Summary: This study examines the environmental impacts of three alternative hydrogen-generating processes and determines the best environmentally friendly option for hydrogen production by comparing different impact categories. The results show that the solar-based HyS cycle options perform the best in terms of global warming potential, abiotic depletion, acidification potential, ozone layer depletion, and human toxicity potential.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
LaGrande Gunnell, Bethany Nicholson, John D. Hedengren
Summary: A review of current trends in scientific computing shows a shift towards open-source and higher-level programming languages like Python, with increasing career opportunities in the next decade. Open-source modeling tools contribute to innovation in equation-based and data-driven applications, and the integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to significantly accelerate progress, but long-term support mechanisms are still necessary.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Daniel Cristiu, Federico d'Amore, Fabrizio Bezzo
Summary: This study presents a multi-objective mixed integer linear programming framework to optimize the supply chain for mixed plastic waste in Northern Italy. Results offer quantitative insights into economic and environmental performance, balancing trade-offs between maximizing gross profit and minimizing greenhouse gas emissions.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
