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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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US Army Aviation air movement operations assignment, utilization and routing

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Journal of Defense Analytics and Logistics 2023年第1期7卷 2-28页

作者： Nelson, Russell King, Russell McConnell, Brandon M. Thoney-Barletta, Kristin Operations Research Graduate Program North Carolina State University Raleigh NC United States Department of Industrial and Systems Engineering North Carolina State University Raleigh NC United States Center for Additive Manufacturing and Logistics North Carolina State University Raleigh NC United States Department of Textile and Apparel Technology and Management North Carolina State University at Raleigh Raleigh NC United States

Purpose: The purpose of this study was to create an air movement operations planning model to rapidly generate air mission request (AMR) assignment and routing courses of action (COA) in order to minimize unsupported AMRs, aircraft utilization and routing cost. Design/methodology/approach: In this paper, the US Army Aviation air movement operations planning problem is modeled as a mixed integer linear program (MILP) as an extension of the dial-a-ride problem (DARP). The paper also introduces a heuristic as an extension of a single-vehicle DARP demand insertion algorithm to generate feasible solutions in a tactically useful time period. Findings: The MILP model generates optimal solutions for small problems (low numbers of AMRs and small helicopter fleets). The heuristic generates near-optimal feasible solutions for problems of various sizes (up to 100 AMRs and 10 helicopter team fleet size) in near real time. research limitations/implications: Due to the inability of the MILP to produce optimal solutions for mid- and large-sized problems, this research is limited in commenting on the heuristic solution quality beyond the numerical experimentation. Additionally, the authors make several simplifying assumptions to generalize the average performance and capabilities of aircraft throughout a flight. Originality/value: This research is the first to solve the US Army Aviation air movement operations planning problem via a single formulation that incorporates multiple refuel nodes, minimization of unsupported demand by priority level, demand time windows, aircraft team utilization penalties, aircraft team time windows and maximum duration and passenger ride time limits. © 2022, In accordance with section 105 of the US Copyright Act, this work has been produced by a US government employee and shall be considered a public domain work, as copyright protection is not available.

关键词： Aircraft Demand priority Dial-a-ride problem Helicopter routing Heuristic Military aviation Multiple refuel nodes Vehicle utilization

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Blockchain: A Review from the Perspective of operations researchers 22

Blockchain: A Review from the Perspective of Operations Rese...

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Proceedings of the Winter Simulation Conference

作者： Hong Wan Kejun Li Yining Huang Department of Industrial and Systems Engineering North Carolina State University Raleigh NC USA Operations Research Graduate Program North Carolina State University Raleigh NC USA

Blockchain is a distributed append-only digital ledger. The technology has caught much attention since the emergence of cryptocurrency, and there is an increasing number of blockchain applications in various businesses. The concept, however, is still novel to many members of the simulation and operations research community. In this tutorial, we introduce the blockchain technology and review its frontier related research. There are exciting opportunities for researchers in simulation, system analysis, and data science.

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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A Framework of Risk Profile in Public Healthcare System Development: A Literature Review 2nd

A Framework of Risk Profile in Public Healthcare System Deve...

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2nd International Symposium on Disaster Resilience and Sustainable Development, 2021

作者： Krutphong, Kodchakorn Tang, Jing Leelawat, Natt Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Bangkok Thailand Risk and Disaster Management Program Graduate School Chulalongkorn University Bangkok Thailand International School of Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789811947148

Risk profile has been utilized in a variety of knowledge fields beyond disaster management or risk management it also plays as the tools in assessment process before the planning both in operation and *** public healthcare system is considered as one of the most critical stakeholders during crises such as disasters and *** risk profile in the public healthcare system requires experts to help developing it according to its *** to a limited number of reviews in the field, this review study focuses on the public healthcare system risk profile’s guideline and development from well-known research *** study also provides the SWOT analysis to realize the strengths, weaknesses, opportunities, and threats of each specific guideline for further *** research results found the ratio of the research in the keywords of the risk profile mostly in the field of medicine and rising number since 2011–*** order to develop the risk profile in such a specific area, it is necessary to use the strength part which suits with the appropriate area. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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A TWO-STAGE FEDERATED LEARNING APPROACH FOR industrial PROGNOSTICS USING LARGE-SCALE HIGH-DIMENSIONAL SIGNALS

arXiv

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arXiv 2024年

作者： Su, Yuqi Fang, Xiaolei Operations Research Graduate Program North Carolina State University RaleighNC United States Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University RaleighNC United States

industrial prognostics aims to develop data-driven methods that leverage high-dimensional degradation signals from assets to predict their failure times. The success of these models largely depends on the availability of substantial historical data for training. However, in practice, individual organizations often lack sufficient data to independently train reliable prognostic models, and privacy concerns prevent data sharing between organizations for collaborative model training. To overcome these challenges, this article proposes a statistical learning-based federated model that enables multiple organizations to jointly train a prognostic model while keeping their data local and secure. The proposed approach involves two key stages: federated dimension reduction and federated (log)-location-scale regression. In the first stage, we develop a federated randomized singular value decomposition algorithm for multivariate functional principal component analysis, which efficiently reduces the dimensionality of degradation signals while maintaining data privacy. The second stage proposes a federated parameter estimation algorithm for (log)-location-scale regression, allowing organizations to collaboratively estimate failure time distributions without sharing raw data. The proposed approach addresses the limitations of existing federated prognostic methods by using statistical learning techniques that perform well with smaller datasets and provide comprehensive failure time distributions. The effectiveness and practicality of the proposed model are validated using simulated data and a dataset from the NASA repository. © 2024, CC BY.

关键词： Principal component analysis

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Enhancing Safety Measures in a Steel Manufacturing Facility;Factory X Case Study

Enhancing Safety Measures in a Steel Manufacturing Facility;...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Mariam Ali Sally S. Kassem Sherif A. Elatriby Industrial Engineering Program Smart Engineering Systems Research Center Nile University Giza Egypt Operations Research and Decision Support Department Faculty of Computers and Artificial Intelligence Cairo Univesityt Giza Egypt Faculty of Engineering Helwan University Helwan Cairo Egypt Industrial Engineering Program Smart Engineering Systems Research Center Nile University Giza Egypt

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Productivity and safety of the workers have been issues in most manufacturing facilities in Egypt and all over the world, as the main aim of any manufacturing facility is to increase profit and limit costs as much as possible. Through analysis and simulation, this paper aims to create a safe workplace for the worker; risk assessment and Failure Mode and Effect Analysis (FMEA) and the basics of the international standards ISO 31000 and ISO 45001 have been conducted to evaluate different hazards and potential risks. Moreover, lean tools such as 6S and Visual Management are proposed solutions to enhance workplace safety and decrease the possibility or severity of potential risks. Therefore, a new workplace design, an example of a high-level hazard in industry in Egypt's local market, has been established for the steel manufacturing facility under consideration using Sketch Up. The results of the proposed solutions, such as new frameworks, worker safety training, equipment replacements, and applying lean tools, have shown a practically acceptable percentage of safety improvements and a decreasing risk priority number (RPN). The percentage decrease for (RPN) ranges between 31.43% to 90.47% for different potential risks.

关键词： Training Productivity Visualization ISO Standards Employment Materials handling Production facilities Hazards Loss measurement Steel

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Virtual Wearable Sensor Data Generation with Generative Adversarial Networks

Virtual Wearable Sensor Data Generation with Generative Adve...

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Simulation Winter Conference

作者： Yining Huang Hong Wan Xi Chen Operations Research Graduate Program North Carolina State University Raleigh NC USA Department of Industrial and Systems Engineering North Carolina State University Raleigh NC USA Grado Department of Industrial and Systems Engineering Virginia Tech Blacksburg VA USA

This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offeringaninnovativealternativetotraditionalmetamodel-basedsimulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs’ capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems.

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Fucoxanthin induces human melanoma cytotoxicity by thwarting the JAK2/STAT3/BCL-xL signaling axis

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Environmental Toxicology 2024年第6期39卷 3356-3366页

作者： Kuo, Min-Yung Dai, Wen-Chyi Chang, Jie-Li Chang, Jo-Shu Lee, Tse-Min Chang, Chia-Che Pediatric Surgery Division Department of Surgery Tungs' Taichung MetroHarbor Hospital Taichung Taiwan Doctoral Program in Biotechnology Industrial Innovation and Management National Chung Hsing University Taichung Taiwan Taichung Municipal Taichung First Senior High School Taichung Taiwan Department of Chemical and Materials Engineering Research Center for Smart Sustainable Circular Economy Tunghai University Taichung Taiwan Department of Life Sciences National Chung Hsing University Taichung Taiwan Department of Marine Biotechnology and Resources National Sun Yat-sen University Kaohsiung Taiwan Graduate Institute of Biomedical Sciences Master Program in Precision Health Doctoral Program in Translational Medicine Rong Hsing Research Center for Translational Medicine The iEGG and Animal Biotechnology Research Center National Chung Hsing University Taichung Taiwan Department of Medical Laboratory Science and Biotechnology Asia University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Traditional Herbal Medicine Research Center Taipei Medical University Hospital Taipei Taiwan

Melanoma is the most lethal skin malignancy. Fucoxanthin is a marine carotenoid with significant anticancer activities. Intriguingly, Fucoxanthin's impact on human melanoma remains elusive. Signal Transducer and Activator of Transcription 3 (STAT3) represents a promising target in cancer therapy due to its persistent activation in various cancers, including melanoma. Herein, we revealed that Fucoxanthin is cytotoxic to human melanoma cell lines A2758 and A375 while showing limited cytotoxicity to normal human melanocytes. Apoptosis is a primary reason for Fucoxanthin's melanoma cytotoxicity, as the pan-caspase inhibitor z-VAD-fmk drastically abrogated Fucoxanthin-elicited clonogenicity blockage. Besides, Fucoxanthin downregulated tyrosine 705-phosphorylated STAT3 (p-STAT3 (Y705)), either inherently present in melanoma cells or inducible by interleukin 6 (IL-6) stimulation. Notably, ectopic expression of STAT3-C, a dominant-active STAT3 mutant, abolished Fucoxanthin-elicited melanoma cell apoptosis and clonogenicity inhibition, supporting the pivotal role of STAT3 blockage in Fucoxanthin's melanoma cytotoxicity. Moreover, Fucoxanthin lowered BCL-xL levels by blocking STAT3 activation, while ectopic BCL-xL expression rescued melanoma cells from Fucoxanthin-induced killing. Lastly, Fucoxanthin was found to diminish the levels of JAK2 with dual phosphorylation at tyrosine residues 1007 and 1008 in melanoma cells, suggesting that Fucoxanthin impairs STAT3 signaling by blocking JAK2 activation. Collectively, we present the first evidence that Fucoxanthin is cytotoxic selectively against human melanoma cells while sparing normal melanocytes. Mechanistically, Fucoxanthin targets the JAK2/STAT3/BCL-xL antiapoptotic axis to provoke melanoma cell death. This discovery implicates the potential application of Fucoxanthin as a chemopreventive or therapeutic strategy for melanoma management. © 2024 Wiley Periodicals LLC.

关键词： Carotenoids

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS 15

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BAS...

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15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. dos Coelho, Leandro S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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