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Modelling and control of a multi-product pipeline transportation process

International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： control strategies liquid transportation modeling of system

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Groundbreaking taxonomy of metaverse characteristics

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Artificial Intelligence Review 2025年第8期58卷 1-50页

作者： Sadeghi-Niaraki, Abolghasem Rahimi, Fatema Azlan, Nur Alya Emanuelle Binti Song, Houbing Ali, Farman Choi, Soo-Mi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of BaltimoreMD21250 United States Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the Metaverse offering a structured and adaptable framework that extends beyond existing categorizations by incorporating dynamic transformations. Unlike prior taxonomies, which often focus on fixed attributes, our approach emphasizes the dynamic evolution of Metaverse characteristics. Through an extensive review of published literature, this study explores key technological, social, economic, and ethical dimensions of the Metaverse. It introduces a process-oriented classification based on 23 distinct characteristics, including immersification, spatiotemporalification, interactification, persistentification, presentification, personification, unification, imaginification, economification, uncertaintification, and credification. By mapping these evolving aspects, we provide a structured and future-proof foundation for understanding the Metaverse’s continuous development. This survey establishes a new standard for comprehensiveness and innovation, shedding light on the diverse facets that have been explored in literature. Through this novel taxonomy, we provide a detailed map of the current landscape and offer insights that pave the way for future research and development in this burgeoning digital frontier. © The Author(s) 2025.

关键词： Comprehensive analysis Futuristic exploration Metaverse characteristics Paradigm-shifting taxonomy Revolutionary categorization

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The Novel DeepBoost Multi Output Regressor for Household Finance Intelligence

The Novel DeepBoost Multi Output Regressor for Household Fin...

引用

Advanced Research in Computing (ICARC), International Conference on

作者： S. H. S. V. Suwandaratna G. O. Priyankara Department of Computer Science and Engineering Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

This paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system within the economy. Accurate forecasting of household finances assists in maintaining positive financial behavior among individuals and the economy. The DeepBoost multi-output regressor proposed in this paper is based on the 1D CNN-ANN and the XGBoost. The proposed model in this paper is compared with the R 2 , MSE, and MAE since it’s a regression problem. The experimental results reveal that the proposed DeepBoost multi-output regressor has the best application in forecasting the multiple expenditures of households by outperforming the ANN, 1D CNN-ANN, and Random Forest Regressor models. The proposed DeepBoost multi-output regressor evaluated the housing, food, transportation, healthcare, other necessities, childcare, and tax expenditures that had 0.94, 0.98, 0.83, 0.94, 0.97, 0.97, and 0.99 values for the R 2 , 9037.71, 2692.12, 9788, 15077.33, 1373.93, 13629.36, and 1904.52 values for the MSE, and 66.07, 34.05, 73.17, 87.05, 26.25, 78.74, and 29.47 MAE values than the ANN, RFR, and 1D CNN-ANN models.

关键词： Deep learning Pediatrics Computational modeling Finance Transportation Medical services Predictive models Microeconomics Forecasting Random forests

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Efficient Object Detection of Marine Debris using Pruned YOLO Model

arXiv

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arXiv 2025年

作者： Aryaza, Abi Yudistira, Novanto Tibyani Informatics Department Faculty of Computer Science Brawijaya University Malang65145 Indonesia

Marine debris remains a persistent and crucial issue that demands attention, as the substances present in the waste pose significant harm to marine life. Ingredients such as microplastics, polychlorinated biphenyls, and pesticides can poison and damage the habitats of organisms living in proximity. However, solutions involving human labor, such as diving, are becoming increasingly ineffective due to the limitations of humans in underwater environments. To address this challenge, technology involving autonomous underwater vehicles continues to be developed for effective sea garbage collection. In this development process, the selection of object detection architecture in autonomous underwater vehicles plays a critical role. For the creation of robots capable of handling marine debris, a one-stage detector type architecture is highly recommended due to its necessity for real-time detection. This research focuses on utilizing the You Only Look Once model version 4 (YOLOv4) architecture for the object detection of marine debris. The dataset utilized in this research comprises 7683 images of marine debris collected in Trash-ICRA 19 dataset with 480x320 pixels. Furthermore, various modifications, such as using pretrained model, training from scratch, disable mosaic augmentation, enable mosaic augmentation, freezing the backbone only layer, freezing the backbone and neck layer, YOLOv4-tiny, and adding channel pruning of YOLOv4, are implemented each other and compared to find the most impactful for enhance the efficiency of the architecture. Numerous studies have demonstrated that the application of channel pruning can improve detection speed without significantly sacrificing accuracy. The dataset employed in this research is the trash-ICRA 19 dataset, featuring images of objects in seawater. This dataset emphasizes images of plastic waste objects in water, incorporating several other classes. Through the application of channel pruning to YOLOv4 trained on the underwater ob

关键词： Polychlorinated biphenyls

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Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation

arXiv

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arXiv 2025年

作者： Tan, Xinyi Wang, Jiacheng Wang, Liansheng Department of Computer Science School of Informatics Xiamen University Xiamen361005 China

Employing self-supervised learning (SSL) methodologies assumes par-amount significance in handling unlabeled polyp datasets when building deep learning-based automatic polyp segmentation models. However, the intricate privacy dynamics surrounding medical data often preclude seamless data sharing among disparate medical centers. Federated learning (FL) emerges as a formidable solution to this privacy conundrum, yet within the realm of FL, optimizing model generalization stands as a pressing imperative. Robust generalization capabilities are imperative to ensure the model’s efficacy across diverse geographical domains post-training on localized client datasets. In this paper, a Federated self-supervised Domain Generalization method is proposed to enhance the generalization capacity of federated and Label-efficient intestinal polyp segmentation, named LFDG. Based on a classical SSL method, DropPos, LFDG proposes an adversarial learning-based data augmentation method (SSADA) to enhance the data diversity. LFDG further proposes a relaxation module based on Source-reconstruction and Augmentation-masking (SRAM) to maintain stability in feature learning. We have validated LFDG on polyp images from six medical centers. The performance of our method achieves 3.80% and 3.92% better than the baseline and other recent FL methods and SSL methods, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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An informed deep learning model of the Omicron wave and the impact of vaccination

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computers in Biology and Medicine 2025年 191卷 109968-109968页

作者： Shamsara, Elham König, Florian Pfeifer, Nico Methods in Medical Informatics Department of Computer Science University of Tübingen Tübingen72076 Germany

The Omicron (B.1.1.529) variant of SARS-CoV-2 emerged in November 2021 and has since evolved into multiple lineages. Understanding its transmission, vaccine efficacy, and potential for reinfection is crucial. This study examines the dynamics of Omicron in Germany, France, and Italy by employing Physics-Informed Neural Networks to estimate the temporal parameters influencing its spread. We validated the performance of our model using the Root Mean Squared Percent Error (RMSPE). Our analysis revealed significant correlations between specific viral mutations—S371F, T376A, D405N, and R408S—and increased transmission rates in all three countries. These mutations, prevalent in the Omicron BA.2 and BA.3 sublineages, are linked to immune evasion and heightened transmissibility. © 2025 The Author(s)

关键词： COVID-19

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“I Never Imagined a Robot Speaking Urdu:” Exploring the Influence of Language on Robots' Acceptance 25

“I Never Imagined a Robot Speaking Urdu:” Exploring the In...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Sunbul M. Ahmad Muneeb I. Ahmad Carolina Fuentes Toro Nervo Verdezoto Dias Katarzyna Stawarz School of Computer Science and Informatics Cardiff University Cardiff Wales UK Department of Computer Science Swansea University Swansea Wales UK

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

As robots are increasingly becoming part of daily life worldwide, it is important to ensure that they are inclusive and culturally sensitive to accommodate users from different backgrounds. In particular, many countries in the Global South (GS) have yet to explore the integration and benefits of robots, especially for underrepresented language groups such as Urdu. We present an exploratory mixed-methods study that investigates how robots' language affects the social interaction, acceptability and overall perception of robots within Pakistani Urdu-speaking individuals. The findings highlight the importance of language and cultural adaptation, and how these factors influence the acceptance of robots, emphasising the need for more inclusive and religion sensitive technologies designed for the GS users.

关键词： Human-robot interaction Robot sensing systems Cultural differences

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Unveiling the 2022 Southern Pakistan Flood Footprint: Insights from Sentinel Imagery and Land Deformation Analysis

Unveiling the 2022 Southern Pakistan Flood Footprint: Insigh...

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2024 Annual Conference, ASPRS 2024

作者： Sohail, Mohammad Chu, Tianxing Centre for Water Informatics & Technology Lahore University of Management Sciences Pakistan Department of Computer Science Texas A&M University Corpus Christi United States Conrad Blucher Institute for Surveying and Science Texas A&M University Corpus Christi United States

Prior disaster management studies in the Indus flood plain of Pakistan have overlooked the potential of remote sensing for monitoring flood inundation zones over time. Still, there is a significant gap in understanding the impact of the devastating flood disaster on the land surface. Our research addresses this gap by utilizing multi-temporal Sentinel-1A, 2A, and ground-based validation surveys. This study uses the synthetic aperture radar interferometry (InSAR) technique to precisely characterize surface displacements resulting from the 2022 megaflood event in Pakistan. Our approach involves the analysis of SAR images captured from Sentinel-1A ascending passes, offering a comprehensive dataset for estimating land surface subsidence over the worst affected region in the Indus lower flood plain. The study’s findings indicate a subsidence ranging from -229.82 to -7.84 mm. The urban land subsidence along the Indus River was particularly concerning, with some areas experiencing a substantial displacement of -215.26 mm. Significant subsidence, greater than -100 mm, affected 25.2% of the total study area. Agricultural land near the Indus River faced significant land displacement ranging from -180.82 to -7.84 mm due to the impact of floodwater. Additionally, regions with a maximum floodwater depth of 7 m exhibited higher land surface displacement. Floodwater depth was assessed using a Google Earth-based model. The primary causes of land displacement were excessive rainfall and the substantial flow of floodwater debris, requiring thorough attention from policymakers to ensure sustainable urban and agricultural development in the face of climate change. © Author(s) 2025.

关键词： Interferometry

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Less or More: Towards Glanceable Explanations for LLM Recommendations Using Ultra-Small Devices 25

Less or More: Towards Glanceable Explanations for LLM Recomm...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Wang, Xinru Yu, Mengjie Nguyen, Hannah Iuzzolino, Michael Wang, Tianyi Tang, Peiqi Lynova, Natasha Tran, Co Zhang, Ting Sendhilnathan, Naveen Benko, Hrvoje Xia, Haijun Jonker, Tanya R. Department of Computer Science Purdue University West LafayetteIN United States Reality Labs Research Meta Inc. RedmondWA United States Meta Inc. RedmondWA United States Department of Cognitive Science and Design Lab University of California San Diego San DiegoCA United States

ISBN: (纸本)9798400713064

Large Language Models (LLMs) have shown remarkable potential in recommending everyday actions as personal AI assistants, while Explainable AI (XAI) techniques are being increasingly utilized to help users understand why a recommendation is given. Personal AI assistants today are often located on ultra-small devices such as smartwatches, which have limited screen space. The verbosity of LLM-generated explanations, however, makes it challenging to deliver glanceable LLM explanations on such ultra-small devices. To address this, we explored 1) spatially structuring an LLM's explanation text using defined contextual components during prompting and 2) presenting temporally adaptive explanations to users based on confidence levels. We conducted a user study to understand how these approaches impacted user experiences when interacting with LLM recommendations and explanations on ultra-small devices. The results showed that structured explanations reduced users' time to action and cognitive load when reading an explanation. Always-on structured explanations increased users' acceptance of AI recommendations. However, users were less satisfied with structured explanations compared to unstructured ones due to their lack of sufficient, readable details. Additionally, adaptively presenting structured explanations was less effective at improving user perceptions of the AI compared to the always-on structured explanations. Together with users' interview feedback, the results led to design implications to be mindful of when personalizing the content and timing of LLM explanations that are displayed on ultra-small devices. © 2025 Copyright held by the owner/author(s).

关键词： Economic and social effects

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A Review of Human Vulnerabilities in Cyber Security: Challenges and Solutions for Microfinance Institutions

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Journal of Information Security 2025年第1期16卷 114-130页

作者： Evaline Waweru Simon Maina Karume Alex Kibet Department of Computing and Informatics The Cooperative University Nairobi Kenya Department of Computer Science and Information Technology Kabarak University Nakuru Kenya Department of Computing and Informatics Laikipia University Laikipia Kenya

This review examines human vulnerabilities in cybersecurity within Microfinance Institutions, analyzing their impact on organizational resilience. Focusing on social engineering, inadequate security training, and weak internal protocols, the study identifies key vulnerabilities exacerbating cyber threats to MFIs. A literature review using databases like IEEE Xplore and Google Scholar focused on studies from 2019 to 2023 addressing human factors in cybersecurity specific to MFIs. Analysis of 57 studies reveals that phishing and insider threats are predominant, with a 20% annual increase in phishing attempts. Employee susceptibility to these attacks is heightened by insufficient training, with entry-level employees showing the highest vulnerability rates. Further, only 35% of MFIs offer regular cybersecurity training, significantly impacting incident reduction. This paper recommends enhanced training frequency, robust internal controls, and a cybersecurity-aware culture to mitigate human-induced cyber risks in MFIs.

关键词： Human Vulnerabilities Cybersecurity Microfinance Institutions Cyber Threats Cybersecurity Awareness Risk Mitigation

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