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Implementation of Artificial-Intelligence-Powered Tools in data processing for Well Time Estimation and Risk analysis in Mexico and Ecuador Projects

Implementation of Artificial-Intelligence-Powered Tools in D...

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2025 International Petroleum Technology conference, IPTC 2025

作者： Szlapa, S. Douy, C. Sesboue, P. Erives, J. Telles, J. Benavides Delgado, A.A. Paccha, J. Gori, F. Mani, A. Essmat, N. Azuara, O. SLB London United Kingdom SLB Montpellier France SLB Mexico City Mexico SLB Quito Ecuador SLB Dhahran Saudi Arabia

ISBN: (纸本)9781959025436

The introduction of artificial intelligence (AI)-driven tools has revolutionized offset well analysis, particularly in estimating well time for conceptualization and planning phases. Previously labor-intensive manual processes for analyzing legacy data have been transformed into digital predictions of activities, operational sequences, key performance indicators (KPIs), and risk estimations through a web application. This system comprises AI engines that model specific aspects of well construction, working with a user interface that enables control over data and domain parameters. Engineers can develop well designs and, using existing designs as references, the application computes KPIs, forecasts operational sequences, assesses risks, and estimates well durations. By employing probability distributions for each input and iterating the process multiple times, the application generates histograms for each output upon simulation completion. Before AI tools, engineers faced the daunting task of manually analyzing extensive drilling reports, a process that could take weeks. The new web application streamlines this into a swift, efficient process, drastically reducing time and enhancing accuracy. Tested successfully in projects across Mexico and Ecuador, the application has shown significant improvements in efficiency and competitiveness. These AI-driven tools not only save time but also reduce costs and carbon emissions, exemplifying their transformative impact on the oil and gas industry. Copyright 2025, International Petroleum Technology conference.

关键词： Gas industry

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A Smart Manufacturing Conceptual Framework for Digitalising data Collection of the Quality Management System for Regulatory Compliance in the Medical Device Industry 6

A Smart Manufacturing Conceptual Framework for Digitalising ...

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6th International conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Kannaraya, Puneeth S Arora, Manish Chakrabarti, Amaresh Department of Design and Manufacturing Indian Institute of Science Bengaluru India

The medical device manufacturing industry is driven by stringent regulatory compliances and faces challenges in implementing quality management systems. One such challenge is the preparation of the paper-based documentation of all the documents as evidence for certification audits. With the advent of Industry 4.0, the increasing amount of data from the shop floor, with variety and veracity, poses additional challenges to the organization. Various technological interventions have made quality systems suffer from categorizing the incoming data and presenting it in suitable formats. A smart manufacturing conceptual framework with elements of intelligent manufacturing, quality control and smart manufacturing has been developed and proposed. The activities and outcomes of the framework enable the organization to capture data from the shop floor according to the various entities. The TOPSIS decision modeling technique is used to perform the structural validation of the framework. The TOPSIS scores reveal the influence of the framework compared to a conventional process. A statistical test was also performed to emphasize the claim. © 2024 The Authors.

关键词： Smart manufacturing

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Model-Free Quantitative Diagnosis of Internal Short Circuit for Sodium-Ion Batteries With Charging Capacity Difference analysis

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IEEE Transactions on Power Electronics 2025年第8期40卷 11394-11406页

作者： Liu, Gang Gao, Zhengpeng Lin, Mingqiang Liu, Xingtao Wu, Ji Hefei University of Technology School of Automotive and Transportation Engineering Hefei230009 China Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province Hefei230009 China Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Fujian Institute of Research on the Structure of Matter Jinjiang362200 China

Thermal runaway caused by the internal short circuit (ISC) poses a significant safety risk for sodium-ion batteries (SIBs) in electric vehicles and energy storage applications. Early detection of ISC faults is considered a potential way to reduce the risk associated with fire or explosion and can be identified via minor power leakage. However, diagnosing these leaks in the initial stages of ISC is quite challenging without battery modeling or preliminary experiments. Here, a model-free method based on charging capacity difference analysis is proposed for quantitatively diagnosing ISC fault. Firstly, a wavelet denoising algorithm with improved thresholding is presented to extract the real signals from the noise-containing voltage and current data. Subsequently, the ISC resistance is estimated by calculating the charging capacity difference variation between the ISC cell and the normal cell. Furthermore, the amount of power leakage during the charging process of ISC batteries is considered to improve the accuracy of the ISC diagnosis. Finally, experiments with elaborate ISC batteries are conducted to validate the method, achieving a resistance estimation error below 5% across different ISC severities while maintaining robustness to capacity inconsistencies. Compared to existing model-free methods, the proposed method significantly enhances diagnostic accuracy and broadens its applicability. © 2025 IEEE.

关键词： State of charge

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Comparison of Low and High Fidelity Dynamic modeling Software for Small UAS Based on Flight data

Comparison of Low and High Fidelity Dynamic Modeling Softwar...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Powers, Matthew Baruth, Adam Hantla, Avery University of Kansas Aerospace Engineering LawrenceKS66045 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper is a comparison of aircraft analysis techniques of the H-King Bixler 1.1, a small fixed-wing UAS. Geometric characteristics including wing, fuselage, control surfaces, and moments of inertia were measured, estimated, and used to develop physics-based dynamic models for the H-King Bixler. The dynamic analysis was performed using Advanced Aircraft analysis, as well as Athena Vortex Lattice. Limited, selected high-impact stability derivatives were also estimated using the hpMusic computational fluid dynamics (CFD) solver. A linear time-invariant state-space model of the aircraft was created for each physics-based model. A validation flight test was conducted to assess the quality of engineering level and conceptual methods. Overall, the physics-based modeling provided a relatively acceptable prediction for longitudinal and lateral aircraft states. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Fixed wings

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Coarse-to-fine hybrid network for robust medical image registration in the presence of large deformations

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BIOMEDICAL SIGNAL processING AND control 2025年 100卷

作者： Chen, Dong Gao, Zijian Liu, Jing Song, Tao Li, Lijuan Tian, Liang Hebei Normal Univ Coll Comp & Cyber Secur Shijiazhuang 050025 Hebei Peoples R China Hebei Normal Univ Hebei Prov Engn Res Ctr Supply Chain Big Data Anal Shijiazhuang 050025 Hebei Peoples R China Hebei Chest Hosp Publ Hlth Dept Shijiazhuang 050048 Hebei Peoples R China Hainan Hosp PAP Dept Psychol Haikou 570203 Hainan Peoples R China Hebei Normal Univ Hebei Key Lab Computat Math & Applicat Shijiazhuang 050025 Hebei Peoples R China

Medical image registration is a fundamental and core technology in the field of medical image processing and analysis. In recent years, deep learning-based registration methods have made significant progress and gradually replaced traditional registration methods. However, many existing methods still face challenges when dealing with large deformations caused by significant structural differences or topological changes. To address this issue, we propose a competitive hybrid registration network. Specifically, our network combines the advantages of Vision Transformer (ViT) and Convolutional Neural Network (CNN) in feature extraction, allowing it to enhance long-range dependencies between images while preserving the integrity of local features. Furthermore, we have developed a novel coarse-to-fine registration strategy in which the deformation fields (DFs) are refined dynamically to obtain more accurate and robust estimation. We extensively evaluated our method on multiple registration tasks, and the experiments demonstrate that our approach outperforms other deep learning methods in terms of accuracy, robustness, and generalization.

关键词： Medical image registration Large deformations Interactive fusion attention Multi-scale fusion Accumulation enhancement

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Comparative analysis and application of soft sensor models in domestic wastewater treatment for advancing sustainability

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ENVIRONMENTAL TECHNOLOGY 2025年第11期46卷 1959-1980页

作者： Qiu, Cheng Huang, Fang-qian Zhong, Yu-jie Wu, Ju-zhen Li, Qiang-lin Zhan, Chun-hong Zhang, Yu-fan Wang, Liting Chengdu Technol Univ Dept Mat & Environm Engn Chengdu Peoples R China Key Lab Treatment Special Wastewater Sichuan Prov Chengdu Peoples R China Chengdu Text Coll Dept Light Ind & Mat Chengdu Peoples R China Support Ctr Atmospher Pollut Prevent Sichuan Prov Chengdu Peoples R China Huicai Environm Technol Co Ltd Chengdu Peoples R China

This study focuses on the development and evaluation of soft sensor models for predicting NH3-N values in a wastewater treatment process. The study compares the performance of linear regression (LR), neural networks (NN) and random forest regression (RFR) models. The proposed methodology involves optimizing the sequencing batch reactor process using artificial intelligence and an automatic control system. Real-time NH3-N values are obtained by inputting data from electronic conductivity and temperature sensors into the prediction models. Once the predicted NH3-N value falls below the effluent standard, the cycle ends, improving energy efficiency and sustainability by cutting down the agitator and aerator. The research results demonstrate that the RNN-based NH3-N soft sensor built in this study exhibits the best performance, which is promising for wastewater treatment process optimization and evaluation. The results show that sensor model NNR[0.5Y]H exhibits exceptional performance, utilizing recurrent neural network with 5-step input delays. Sensor NNR[0.5Y]H exhibits an R-2 of 0.921, an RMSE of 6.110, and an MAE of 4.558. Based on the findings, recurrent neural network (RNN) variants emerge as the most effective modeling technique due to their ability to capture temporal dependencies and handle variable-length sequences. This study provides satisfied performance results for the NNR[0.5Y]H soft sensor model in NH3-N monitoring and process optimization in wastewater treatment, highlighting the effectiveness of recurrent neural networks and their contribution to improving interpretability, accuracy, and adaptability of soft sensor models.

关键词： Soft sensors NH3-N prediction linear regression recurrent neural networks random forest regression

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A Combined GAN and DF Based Expansion and Prediction for Small Sample data 16th

A Combined GAN and DF Based Expansion and Prediction for Sma...

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16th International conference on Modelling, Identification and control, ICMIC 2024

作者： Wang, Li Lu, Junfeng Feng, Shanfang Wang, Xiaoyi Jin, Xuebo Yu, Jiabin Bai, Yuting Beijing Laboratory for Intelligent Environmental Protection School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9789819617760

The high cost and difficulty of data collection result in discrete intermittency and limited available data for most datasets, posing challenges to modeling and prediction needs. Thus, it is crucial to explore methodologies suitable for discrete small sample data. Concurrently, unsupervised learning methods are employed for small sample prediction to circumvent the issue of low prediction accuracy resulting from inadequate a priori knowledge. Current data modeling and prediction methods rely heavily on neural network deep learning models, which are mainly designed for continuous processes and show low accuracy for intermittent processes. This paper proposes a data expansion method based on Time Generative Adversarial Networks (TimeGAN) and a prediction method based on the combination of Generative Adversarial Networks (GAN) and Deep Forest (DF). Firstly, the TimeGAN model is proposed to learn small sample data for data expansion. Then, the GAN-DF model is constructed so that the model is used for data prediction of unsupervised discrete processes. On this basis, we introduce the DFGAN and LSTM-DFGAN models, which embed DF and LSTM-DF models as generators within the GAN framework to enhance prediction accuracy for small sample data. Simulation validation using contaminant data from a food processing process demonstrates the efficacy of the TimeGAN method in data expansion. The results demonstrate that the TimeGAN method is capable of effectively expanding the data, while the LSTM-DFGAN model exhibits the most optimal combined prediction effect. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Recommendations for developing, documenting, and distributing data products derived from NEON data

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ECOSPHERE 2025年第1期16卷

作者： Atkins, Jeff W. Aho, Kelly S. Chen, Xuan Elmore, Andrew J. Fiorella, Rich Luo, Wenqi Lombardozzi, Danica Lunch, Claire Manak, Leah de Pablo, Luis X. Myers-Pigg, Allison N. Record, Sydne Qiu, Tong Reed, Samuel Ruddell, Benjamin Strange, Brandon Torrens, Christa L. Yule, Kelsey Richardson, Andrew D. USDA Forest Serv Southern Res Stn New Ellenton SC 29809 USA Virginia Commonwealth Univ Dept Biol Richmond VA 23284 USA Michigan State Univ Dept Earth & Environm Sci E Lansing MI USA Michigan State Univ Dept Integrat Biol E Lansing MI USA Salisbury Univ Dept Biol Sci Salisbury MD USA Univ Maryland Ctr Environm Sci Appalachian Lab Frostburg MD USA Los Alamos Natl Lab Los Alamos NM USA Univ Michigan Sch Environm & Sustainabil Ann Arbor MI USA Natl Ctr Atmospher Res Boulder CO USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO USA Natl Ecol Observ Network Boulder CO USA No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ USA Univ Colorado BioFrontiers Inst Boulder CO USA Pacific Northwest Natl Lab Marine & Coastal Res Lab Sequim WA USA Univ Maine Dept Wildlife Fisheries & Conservat Biol Orono ME USA Duke Univ Nichoals Sch Environm Durham NC USA Univ Minnesota Nat Resources & Management St Paul MN USA Univ Montana Flathead Lake Biol Stn Polson MT USA Arizona State Univ Sch Life Sci Tempe AZ USA No Arizona Univ Ctr Ecosyst Sci & Soc Flagstaff AZ USA

The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products-higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly-generated datasets created by applying various processing steps to one or more lower level data products-including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.

关键词： community science data derived data products NEON observatory science Special Feature: Harnessing the NEON data Revolution

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23rd International conference on Informatics in Economy, IE 2024

23rd International Conference on Informatics in Economy, IE ...

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23rd International conference on Informatics in Economy, IE 2024

ISBN: (纸本)9789819601608

The proceedings contain 39 papers. The special focus in this conference is on Informatics in Economy. The topics include: Performance Evaluation of data Vault and Dimensional modeling: Insights from TPC-DS dataset analysis;revolutionizing Healthcare: Harnessing Natural Language processing and Big data for Predictive Disease Diagnosis;enhancing the Involvement Level of Volunteer Students in the Academic Community: Efficient Usage of Digital Resources;intelligent Models in Power Delivery Management for Economic Damage Computation;smart City Parking Applications in Romania: An analysis and Conclusions;measurement and Verification Solution for Energy Performance Contracting in the Building Sector;analyzing the Antecedents of Deceived Buying and Disinformation Risk;driving Factors of Social Commerce Intention: The Role of Social Commerce Constructs, Social Influence, and Trust;strategic Collaboration in International University Networks–Case Study;Comparative analysis of Natural Language Query Responses on BPMN Model Serializations: RDF Graphs Versus BPMN XML;a Hybrid Retrieval-Augmented Generation Approach for Heterogeneous Knowledge Bases;digitalization in Higher Education—Designing and Implementing an Application to Facilitate the University Final Thesis Collaboration process;an Overlook of IoT Finance and Pre-existing Security Challenges in Financial Transactions via IoT Devices on Blockchain Networks;models for Network Traffic Behavioral analysis in IoT Systems;augmented Reality in Education—Prototype for the Undergraduate System in Romania;empowering Sustainability: Upcycling Smartphones as the Future of IoT and Edge Computing in Emerging Economies;quantum Computing and Cybersecurity: Threat or Opportunity?;new Real-Time Encryption Mechanism for Financial Transactions;enhanced Blockchain-Based e-Voting System Using Zero-Knowledge Proofs;The Environmental Kuznets Curve for Deforestation in Romania: An ARDL-Based Evaluation.

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Coupled magneto-mechanical growth in hyperelastic materials: Surface patterns modulation and shape control in bio-inspired structures

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JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS 2025年 200卷

作者： Li, Zhanfeng Wang, Yafei Wang, Zuodong Kadapa, Chennakesava Hossain, Mokarram Yao, Xiaohu Wang, Jiong South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510640 Peoples R China South China Univ Technol State Key Lab Subtrop Bldg & Urban Sci Guangzhou 510640 Peoples R China Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen 518055 Peoples R China Edinburgh Napier Univ Sch Comp Engn & Built Environm Edinburgh EH10 5DT Scotland Swansea Univ Zienkiewicz Inst Modelling Data & AI Fac Sci & Engn Swansea SA1 8EN Wales

Magneto-mechanical coupling in the growth of soft materials presents challenges due to the complex interactions between magnetic fields, mechanical forces, and growth-induced deformations. While growth modeling has been extensively studied, integrating magnetic stimuli into growth processes remains underexplored. In this work, we develop a 3D governing system for capturing the coupled magneto-mechanical growth behaviors of soft materials. Based on the governing system, we propose a finite element framework, where the robustness and accuracy of the proposed framework are demonstrated through numerical simulations, including the uniaxial loading of a circular tube, a mesh convergence study, and surface pattern evolution. We also conduct experiments on surface pattern modulation in magneto- active soft materials. Specifically, we fabricate film-substrate samples and apply growth-induced instabilities combined with external magnetic fields to generate tunable surface patterns. To demonstrate the capabilities of our method, we apply our numerical framework to mimic the biological morphogenesis, such as the inversion process of the algal genus Volvox. Our study shows that integrating magneto-mechanical coupling with growth effects allows for flexible control over surface patterns, significantly enhancing the adaptability and responsiveness of soft materials. This work paves the way for innovative designs of adaptive and programmable soft materials, with potential applications in soft robotics, biomimetic structures, and tissue engineering.

关键词： Magneto-elasticity Differential growth Finite element analysis Surface pattern evolution Shape control

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