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Eye tracking data analytics in virtual reality training: Application in Deepwater Horizon oil drilling operation

Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2021年第1期64卷 821 - 825页

作者： Ziho Kang Jiwon Jeon Saeed Salehi Industrial and Systems Engineering University of Oklahoma Norman OK Data Science and Analytics Program University of Oklahoma Norman OK Petroleum and Geological Engineering University of Oklahoma Norman OK

Virtual reality (VR) enable us to train in a safe environment using computer-generated simulations. One such simulated environment is the Deepwater Horizon operation, and VR enables us to evaluate trainees’ and operators’ situation awareness (SA) in a non-hazardous environment. One unobtrusive and viable SA evaluation method might be the use of eye movements, specifically the time-ordered visual scan paths. In this research, we investigated how SA can be associated with visual scan paths in an anomaly detection task within the oil drilling rig virtual reality simulator. The results show that the trainees having lower SA tended to create random visual scan paths whereas the trainees having higher SA tended to create concentrated and refined visual scan paths. The results show promise in developing timely intervention methods through analyzing the visual scan path characteristics of the trainees.

关键词： Eye tracking visual scanning patterns virtual reality training Deepwater Horizon operations situation awareness

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Learning to Simulate: Generative Metamodeling via Quantile Regression

arXiv

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arXiv 2023年

作者： Hong, L. Jeff Hou, Yanxi Zhang, Qingkai Zhang, Xiaowei Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States School of Data Science Fudan University Shanghai200433 China School of Management Fudan University Shanghai200433 China Department of Management Sciences City University of Hong Kong Kowloon Hong Kong Department of Industrial Engineering and Decision Analytics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

Stochastic simulation models effectively capture complex system dynamics but are often too slow for real-time decision-making. Traditional metamodeling techniques learn relationships between simulator inputs and a single output summary statistic, such as the mean or median. These techniques enable real-time predictions without additional simulations. However, they require prior selection of one appropriate output summary statistic, limiting their flexibility in practical applications. We propose a new concept: generative metamodeling. It aims to construct a "fast simulator of the simulator," generating random outputs significantly faster than the original simulator while preserving approximately equal conditional distributions. Generative metamodels enable rapid generation of numerous random outputs upon input specification, facilitating immediate computation of any summary statistic for real-time decision-making. We introduce a new algorithm, quantile-regression-based generative metamodeling (QRGMM), and establish its distributional convergence and convergence rate. Extensive numerical experiments demonstrate QRGMM’s efficacy compared to other state-of-the-art generative algorithms in practical real-time decision-making scenarios. © 2023, CC BY-NC-ND.

关键词： Stochastic models

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Recent research progress on the direct carbon capture of steel slag to prepare building materials

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Green and Smart Mining engineering 2024年第4期1卷 387-395页

作者： Zihe Gao Qing Zhao Mengjie Tao Zengrui Wang Chengjun Liu Henrik Saxén Ron Zevenhoven Key Laboratory for Ecological Metallurgy of Multimetallic Mineral(Ministry of Education) Northeastern UniversityShenyang 110819China School of Metallurgy Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China Process and Systems Engineering Laboratory Åbo Akademi UniversityTurku 20500Finland

Driven by the“dual carbon”and“green metallurgy”targets,the steel industry feels the pressure of carbon emission reduction and steel slag resource *** the year 2000,accelerated carbonation technology using basic oxides in steel slag to store CO_(2)has been widely investigated *** this study,the chemical and mineral compositions and the main physical properties of different steel slags are summarized to achieve the dual purpose of CO_(2)mineralization and steel slag *** paper also summarizes the latest advancements in the direct carbonation of steel *** the carbonation process,residual free calcium oxide and magnesium oxide can be further carbonated to aid in the preparation of building *** direct carbonation method improves the use of steel slag as an aggregate and in cementitious applications,exhibiting effective performance in steel-slag-based *** the limited extent of steel slag carbonation and the modest utilization of available products,this paper explores the current research on carbonated steel slag for building material fabrication and discusses the variety of green building materials.

关键词： Dual carbon Green metallurgy Steel slag Carbonation Building materials

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Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations

arXiv

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arXiv 2023年

作者： Jafarigol, Elaheh Trafalis, Theodore B. Razzaghi, Talayeh Zamankhani, Mona Data Science and Analytics Institute University of Oklahoma 202 W. Boyd St. Room 409 NormanOK73019 United States School of Industrial and Systems Engineering University of Oklahoma 202 W Boyd St. Room 124 NormanOK73019 United States Department of Industrial Engineering Isfahan University of Technology Isfahan Iran

In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning lays the groundwork for collaborative research in areas where the data is sensitive. Federated learning has several implications for real-world problems. In times of crisis, when real-time decision-making is critical, federated learning allows multiple entities to work collectively without sharing sensitive data. This distributed approach enables us to leverage information from multiple sources and gain more diverse insights. This paper is a systematic review of the literature on privacy-preserving machine learning in the last few years based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have presented an extensive review of supervised/unsupervised machine learning algorithms, ensemble methods, meta-heuristic approaches, blockchain technology, and reinforcement learning used in the framework of federated learning, in addition to an overview of federated learning applications. This paper reviews the literature on the components of federated learning and its applications in the last few years. The main purpose of this work is to provide researchers and practitioners with a comprehensive overview of federated learning from the machine learning point of view. A discussion of some open problems and future research directions in federated learning is also provided. © 2023, CC BY.

关键词： Decision making

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Future Glycemic Events Prediction Model Based On Artificial Neural Network

Future Glycemic Events Prediction Model Based On Artificial ...

引用

2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022

作者： Syafrudin, Muhammad Alfian, Ganjar Fitriyani, Norma Latif Hadibarata, Tony Rhee, Jongtae Anshari, Muhammad Sejong University Department of Artificial Intelligence Seoul Korea Republic of Universitas Gadjah Mada Department of Electrical Engineering and Informatics Vocational College Yogyakarta Indonesia Sejong University Department of Data Science Seoul Korea Republic of Curtin University Environmental Engineering Program Miri Malaysia Dongguk University Department of Industrial and Systems Engineering Seoul Korea Republic of Universiti Brunei Darussalam School of Business and Economics Brunei

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Predicting future glycemic events such as hypoglycemia, hyperglycemia, and normal for type 1 diabetes (T1D) remains a significant and challenging issue. In this study, an artificial neural network (ANN)-based model is proposed to predict the future glycemic events of T1D patients. We utilized five T1D patient datasets to build the models and predict future glycemic events with a prediction horizon (PH) of 30 and 60 minutes ahead of time. We applied the data preprocessing method based on the sliding window approach by sliding the blood glucose time-series data from the past 60 minutes (the last 12 data points) as input and using the next 30 and 60 minutes (the next 6 and 12-th data points) as output. All the numeric blood glucose output data are then transformed into a multi-class classification label, such as hypoglycemia, hyperglycemia, and normal. Our proposed model is then used to learn and create the prediction model from the preprocessed blood glucose dataset. Four performance metrics such as accuracy, precision, recall, and f-1 score were utilized to measure the performance of the classification models used in this study, such as Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN). The results showed that our proposed ANN-based model performed better at predicting future glycemic events than other models, with an average accuracy, precision, recall, and f-1 score of 88.649%, 76.661%, 71.731%, 72.609%, and 83.364%, 60.437%, 61.345%, 60.62% for the PH of 30 and 60 minutes, respectively. As a result, knowing this future glycemic event sooner can help patients avoid potentially dangerous conditions and can eventually be used to improve diabetes management. © 2022 IEEE.

关键词： Decision trees

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Exploring cognitive stress-related tasks of Intensive Care Unit nurses – A comparison study 67th

Exploring cognitive stress-related tasks of Intensive Care U...

引用

67th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2023

作者： Zhang, Qian Sasangohar, Farzan Saravanan, Pratima Ahmadi, Nima Nisar, Tariq Danesh, Valerie Masud, Faisal Center for Health Data Science and Analytics Houston Methodist Hospital TX United States Department of Industrial and Systems Engineering Texas A&M University College StationTX United States Rensselaer Polytechnic Institute TroyNY United States Baylor Scott & White Health Center for Applied Health Research DallasTX United States School of Nursing University of Texas at Austin AustinTX United States Center for Critical Care Houston Methodist Hospital HoustonTX United States College of Charleston CharlestonNY United States

The aim of this study is to explore nurse activities leading to high cognitive stress for intensive care unit (ICU) nurses. The quantification of stress-related nurse activities has been acquired before and after the working shift, with a general gap in analyzing the whole shift. To address this gap, the task analysis of stress-related nurse activities during whole shift was conducted for two participants. The administration tasks (processing clinical data, exchanging professional information and ordering examination), and communicating with patients were most performed when under high cognitive stress. Future work should investigate the contribution of administration and patient-related tasks to high cognitive stress for ICU nurses. © 2023 Human Factors and Ergonomics Society.

关键词： cognitive stress nurse activity physiological measurements

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Machine Learning, Deep Learning and data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review

arXiv

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arXiv 2023年

作者： Razavi, Moein Ziyadidegan, Samira Mahmoudzadeh, Ahmadreza Kazeminasab, Saber Baharlouei, Elaheh Janfaza, Vahid Jahromi, Reza Sasangohar, Farzan Department of Industrial and Systems Engineering Texas A&M University United States Department of Computer Science and Engineering Texas A&M University United States Harvard Medical School Harvard University United States Department of Computer Science University of Houston United States Engineering Academic and Student Affairs College of Engineering Texas A&M University United States Ford Motor Company Global Data Insight & Analytics

Background: Mental stress and its consequent mental disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there's potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies employed in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent mental disorders. Objective: This review aims to investigate the scope of Machine Learning (ML) methodologies employed in the detection, prediction, and analysis of mental stress and its consequent mental disorders (MDs). Methods: Utilizing a rigorous scoping review process with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extention for Scoping Reviews (PRISMA-ScR) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types employed in the context of stress and stress-related MDs. Results and Discussion: Total of 98 peer-reviewed publication were examined for this review. The findings highlight that Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) models consistently exhibit superior accuracy and robustness among all machine learning algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms. Conclusion: The synthesis of this review identifies significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the i

关键词： Dimensionality reduction

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Facile synthesis of flower-like bismuth oxybromide anchored on ZSM-5 for enhanced photocatalytic dye degradation

引用

South African Journal of Chemical engineering 2025年 53卷 386-399页

作者： Wahyu Prasetyo Utomo Nor Farida Binta Aisyah Ahmad Azzam Babgei Nur Karimah Ade Irma Rozafia Imam Susilo Tri Widjaja Hadi Nur Retno Sari Rui Liu Djoko Hartanto Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember (ITS) Sukolilo Surabaya 60111 East Java Indonesia Faculty of Medicine Universitas Airlangga Surabaya 60132 East Java Indonesia Department of Chemical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember (ITS) Sukolilo Surabaya 60111 East Java Indonesia Department of Chemistry Faculty of Mathematics and Natural Science Universitas Negeri Malang Malang Indonesia Department of Pharmaceutics Faculty of Pharmacy Airlangga University Surabaya Indonesia School of Energy and Environment City University of Hong Kong Kowloon 999077 Hong Kong

The morphology of the photocatalyst plays an important role in determining its photocatalytic activity. Bismuth oxybromide (BiOBr) offers the advantages of a unique layered structure, easy preparation, and narrow band gaps. However, a fine morphology of flower-like BiOBr is generally prepared using a relatively complex method, requiring high energy consumption. In this work, we report a facile method to prepare fine flower-like BiOBr, employing a coprecipitation method at room temperature with the addition of polyvinyl pyrrolidone as a capping agent. Further treatment by adding ZSM-5 during the synthesis process leads to the growth of flower-like BiOBr on the surface of ZSM-5, forming a BiOBr/ZSM-5 composite. Photocatalytic test over flower-like BiOBr and flower-like BiOBr/ZSM-5 showed an appreciable performance, reaching the degradation efficiency of methylene blue up to 50.96 % and 87.29 %, respectively upon 120 min of UV illumination, which is higher than the bare irregular BiOBr (31.82 %). The enhanced photodegradation performance can be attributed to the more efficient charge transfer and enlarged surface area, induced by the thin flower-like morphology of BiOBr and the presence of ZSM-5. This work provides insight into facile synthesis and surface modification of BiOBr to enhance its catalytic activity.

关键词： Bismuth oxyhalide Zeolite Photocatalysis Waste water

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Big data in healthcare:Conceptual network structure,key challenges and opportunities

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Digital Communications and Networks 2023年第4期9卷 856-868页

作者： Leonardo B.Furstenaua Pedro Leivas Michele Kremer Sott Michael S.Dohan Jose Ricardo Lopez-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Post-Graduate Program in Industrial Engineering Federal University of Rio Grande Do SulPorto Alegre90035-190Brazil University of Santa Cruz Do Sul Av.Independencia 2293Santa Cruz Do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Faculty of Business Administration Lakehead UniversityThunder BayOntarioCanada Autonomous University of Zacatecas Av.Ramon Lopez Velarde Col.CentroZacatecas98000Mexico Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain York University ON M3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.

关键词： Big data Healthcare digitalization Bibliometric Strategic intelligence Co-word analysis SciMAT

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Ultra-low-frequency gravitational waves from individual supermassive black hole binaries as standard sirens

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Journal of Cosmology and Astroparticle Physics 2025年第5期2025卷 095-095页

作者： Wang, Ling-Feng Shao, Yue Xiao, Si-Ren Zhang, Jing-Fei Zhang, Xin Liaoning Key Laboratory of Cosmology and Astrophysics College of Sciences Northeastern University Shenyang 110819 China School of Physics and Optoelectronic Engineering Hainan University Haikou 570228 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang 110819 China

Ultra-low-frequency gravitational waves (GWs) generated by individual inspiraling supermassive black hole binaries (SMBHBs) at the centers of galaxies may be detected by pulsar timing arrays (PTAs) in the future. These GW signals, which encode absolute cosmic distances, can serve as bright and dark sirens, potentially evolving into a precise cosmological probe. Here, we show that a PTA in the era of the Square Kilometre Array, comprising 100 millisecond pulsars, could potentially detect about 25 bright sirens and 41 dark sirens over a 10-year observation period. The bright sirens, combined with cosmic microwave background data, offer capabilities comparable to current mainstream joint cosmological observations for measuring the equation of state of dark energy. The dark sirens could achieve a measurement precision of the Hubble constant close to that of current distance-ladder observations. Our results suggest that ultra-low-frequency GWs from individual SMBHBs are of great significance in investigating the nature of dark energy and determining the Hubble constant. © 2025 IOP Publishing Ltd and Sissa Medialab. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： dark energy experiments dark energy theory gravitational waves / experiments

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