Characterization of EAF and LF Slags Through an Upgraded Stationary Flowsheet Model of the Electric Steelmaking Route by Ismael Matino, Alice Petrucciani, Antonella Zaccara, Valentina Colla, Maria Ferrer Prieto, Raquel Arias Pérez

“…In this paper, a stationary flowsheet model of the electric steelmaking route is presented; this model enables joint monitoring of key variables related to process, steel and slags. …”
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Multi-model learning for vessel ETA prediction in inland waterways using multi-attribute data by Abdullah Al Noman, Anton Zitnikov, Aaron Heuermann, Klaus-Dieter Thoben

“…Existing ETA prediction models largely rely on Automatic Identification System (AIS) data but often overlook additional factors. …”
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Overhead Transmission Line Modeling Strategies for EMT-Based Traveling-Wave Analysis and Fault Location by Adrian Wilmer Diaz Sarmiento, Jaimis Sajid Leon Colqui, Jose Pissolato Filho

“…The results show that: 1) uniform soil resistivity assumptions introduce negligible errors in TW arrival times despite minor amplitude variations; 2) shield wires significantly affect modal structure, compromising the effectiveness of Clarke transformation for ground quasi-mode decoupling while preserving aerial quasi-mode reliability; 3) exact eigenvector-based decomposition improves ground mode identification but remains impractical for field applications; 4) the classical two-terminal fault location method maintains high accuracy across all modeling configurations; and 5) simplified OHTL modeling uniformly distributed sections at both terminals achieves an optimal balance between accuracy and computational efficiency for simulating TWs. …”
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Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples by Abdullahi G. Usman, Sagiru Mati, Hanita Daud, Ahmad Abubakar Suleiman, Sani I. Abba, Hijaz Ahmad, Taha Radwan

“…The dataset was collected from secondary sources and used to train and validate the SVR-HHO and SVR-PSO models. The performance of the models was assessed via mean square error, correlation coefficient, and Nash–Sutcliffe efficiency. …”
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Surrogate modeling of electrospun PVA/PLA nanofibers using artificial neural network for biomedical applications by Reyhaneh Fatahian, Rasool Erfani

“…Given the high costs and time associated with conducting extensive experimental tests, an artificial neural network based surrogate model is developed to predict experimental outcomes more efficiently, facilitating faster identification of optimal design configurations. …”
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Data-driven modeling approaches for pressure drop prediction in a multi-phase flow system by Nezar M. Alyazidi, Aiman F. Bawazir, Ala S. AL-Dogai

“…Accurate prediction of pressure drops in multi-phase flow systems is essential for optimizing processes in industries such as oil and gas, where operational efficiency and safety depend on reliable modeling. Traditional models often need help with the complexities of multi-phase flow dynamics, resulting in high relative errors, particularly under varying flow regimes. …”
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Estimating Chlorophyll-<i>a</i> and Phycocyanin Concentrations in Inland Temperate Lakes across New York State Using Sentinel-2 Images: Application of Google Earth Engine for Effic... by Sara Akbarnejad Nesheli, Lindi J. Quackenbush, Lewis McCaffrey

“…The fit of the Chl-a models was generally poorer, but these models still had good accuracy in detecting moderate and high Chl-a values. …”
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Developing an Algorithm for Detecting Suspicious Trades in Tehran Stock Exchange Based on Spoof Trading Model by Reza Tehrani, Saeed Fallahpour, Hamid Nouralidokht

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Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge Embedding by Haoqing LI, Dian YU, Changchun PAN, Wenxian YU, Dongying LI

“…The ability to quickly and comprehensively extract common features of multiradar scheduling problems is essential for improving the efficiency of such AI scheduling algorithms. Therefore, this paper proposes a Model Knowledge Embedded Graph Neural Network (MKEGNN) scheduling algorithm. …”
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Designing an educational model based on identity development with an Iranian-Islamic approach for primary school students. by Ali Oladhamzehzadeh, masoumeh oladiyan, Mahmoud Safari

“…Abstract The aim of the current research is to design an educational model based on identity development with an Iranian-Islamic approach for elementary school students in district five of Tehran. …”
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Modeling of Ultrasonic Flaw Detection Processes in the Task of Searching and Visualizing Internal Defects in Assemblies and Structures by B. V. Sobol, A. N. Soloviev, P. V. Vasiliev, A. A. Lyapin

“…Its objective is to develop and implement nondestructive testing methods based on a neural network device to improve the accuracy of defect identification, as well as to build a neural network model and evaluate its effectiveness for the refinement of ultrasonic visualization of internal defects in solid materials. …”
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Optimal selection of high-production well targets for fault-controlled fractured-vuggy reservoir in Shunbei oil and gas field, Tarim Basin by Lijun GAO, Haiying LI, Wei GONG, Wei YANG, Hongyan LI

“…Through comparative analysis of the internal structural characteristics and seismic response variations of different regions and different types of strike-slip fault zones, integrated with actual well seismic calibration statistics and forward modeling, this study established a robust seismic identification model for high-yield and stable production wells. …”
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Quantifying solid volume of stacked eucalypt timber using detection-segmentation and diameter distribution models by Gianmarco Goycochea Casas, Zool Hilmi Ismail, Mathaus Messias Coimbra Limeira, Carlos Pedro Boechat Soares, José Marinaldo Gleriani, Daniel Henrique Brada Binoti, Carlos Alberto Araújo Júnior, Mohd Ibrahim Shapiai, Leonardo Ippolito Rodrigues, Tassius Menezes Araújo, Helio Garcia Leite

“…The proposed procedure combines automatic log detection and diameter distribution models. Automatic log detection was achieved using advanced computer vision techniques, specifically the You Only Look Once version 9 (YOLOv9) model, which automates the identification and counting of individual logs within a stack. …”
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Presenting the entrepreneurship development model in the national oil refining industry with the approach of environmental damage prevention by Jalal Abdi, Reza Yousefi Saeedabadi, Maryam Taghvaee Yazdi

“…Abstract The aim of this research is to investigate the development model of entrepreneurship in the national oil refining industry with the approach of preventing environmental damage. …”
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Investigating the Capabilities of Ensemble Machine Learning Model in Identifying Near-Fault Pulse-Like Ground Motions by Jafar Al Thawabteh, Jamal Al Adwan, Yazan Alzubi, Ahmad Al-Elwan

“…This study applies various ensemble machine learning models, such as random forests, gradient boosting machines, and extreme gradient boosting, for the identification and characterization of pulse-like ground motions. …”
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Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans by Wisal Zafar, Ghassan Husnain, Abid Iqbal, Ali Saeed Alzahrani, Muhammad Abeer Irfan, Yazeed Yasin Ghadi, Mohammed S. AL-Zahrani, Ramasamy Srinivasaga Naidu

“…To address these challenges, we propose, a hybrid deep learning model that precisely segmented tumor regions with U-Net to enable YOLOv8s to efficiently detect, classify and localize tumors. …”
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Hotspots and super-spreaders: Modelling fine-scale malaria parasite transmission using mosquito flight behaviour. by Luigi Sedda, Robert S McCann, Alinune N Kabaghe, Steven Gowelo, Monicah M Mburu, Tinashe A Tizifa, Michael G Chipeta, Henk van den Berg, Willem Takken, Michèle van Vugt, Kamija S Phiri, Russell Cain, Julie-Anne A Tangena, Christopher M Jones

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Business Process Reengineering based on Information Economics by syifa annastasia, Sinung Suakanto, Muharman Lubis

“…A case study in a large organization was conducted to test the effectiveness of the redesigned model. The key findings of this study are its greatest strength and must be explicitly highlighted to convey its impact: the redesigned process resulted in a 67.3% reduction in processing time and a Return on Investment (ROI) of 1,085.17% demonstrating not only operational efficiency but also clear financial gain. …”
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MW‐SAM:Mangrove wetland remote sensing image segmentation network based on segment anything model by Yu Zhang, Xin Wang, Jingye Cai, Qun Yang

“…This paper proposes a novel semantic segmentation framework for mangrove wetlands called mangrove wetland remote sensing image segmentation network based on segment anything model (MW‐SAM) to address these issues. MW‐SAM is based on the pre‐trained SAM and achieves cross‐domain adaptation through parameter‐efficient fine‐tuning techniques. …”
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Iterative segmentation and classification for enhanced crop disease diagnosis using optimized hybrid U-Nets model by Malathi Chilakalapudi, Sheela Jayachandran

“…Feature selection itself is innovated by the introduction of the Moving Gorilla Remora Algorithm (MGRA) combined with convolutional operations, setting a new benchmark in the selection of optimal features pertaining to disease identification operations. To further refine this model, classification is adeptly handled by a process inspired by the LeNet architecture, significantly improving identification against various diseases. …”
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