A Multi-Layer Attention Knowledge Tracking Method with Self-Supervised Noise Tolerance by Haifeng Wang, Hao Liu, Yanling Ge, Zhihao Yu

“…In the pre-training stage, MASKT uses a random forest method to filter out positive and negative correlation feature embeddings; then, it reuses noise-processed restoration tasks to extract more learnable features and enhance the learning ability of the model. …”
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The Influence of Process and Slag Parameters on the Liquid Slag Layer in Continuous Casting Mold for Large Billets by Zhijun Ding, Chao Wang, Xin Wang, Pengcheng Xiao, Liguang Zhu, Shuhuan Wang

“…To optimize the flow and heat transfer behavior in the mold, a three-dimensional numerical model was developed based on the VOF multiphase flow model, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>−</mo><mi>ϵ</mi></mrow></semantics></math></inline-formula> RNG turbulence model, and DPM discrete phase model, employing the finite volume method with SIMPLEC algorithm for solution. …”
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Protection layer-assisted cross-sectional observation method for micro-processed polymer optical fibers by Ryo NAKASHIMA, Takuto NAKANISHI, Keito ISHIDA, Cheng-Yao LO, Heeyoung LEE, Yousuke MIZUNO, Daisuke YAMANE

“…Epoxy resin was used as a protection layer to maintain the structural integrity of the fibers during the cutting process. …”
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Adjoint‐Based Online Learning of Two‐Layer Quasi‐Geostrophic Baroclinic Turbulence by F. E. Yan, H. Frezat, J. Le Sommer, J. Mak, K. Otness

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Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process by Orsolya Péterfi, Nikolett Kállai-Szabó, Kincső Renáta Demeter, Ádám Tibor Barna, István Antal, Edina Szabó, Emese Sipos, Zsombor Kristóf Nagy, Dorián László Galata

“…In this study, an artificial intelligence-based machine vision system was developed for in-line particle size analysis during the pellet layering process. Drug-layered pellets were produced by coating microcrystalline cellulose cores with an ibuprofen-containing layering liquid until the target drug content was achieved. …”
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Predicting the Evolution of Shallow Cumulus Clouds With a Lotka‐Volterra Like Model by Jingyi Chen, Samson Hagos, Jerome Fast, Zhe Feng

“…Abstract In numerical weather prediction and climate models, boundary‐layer clouds are controlled by a wide range of subgrid‐scale processes. …”
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Modeling of Optical Processes in Thin-Film IR Light-Emitting Diode Based on Colloidal PbS Quantum Dots by U. Y. Turavets

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NUMERICAL MODELING OF THE CINDER FORMATION PROCESS WITH APPLICATION OF SYMMETRICAL IMPLICIT SCHEMA IN MOVING HEATED INGOT OF CHROMIUM STEEL by A. N. Chichko, A. S. Borozdin

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Characterization and modeling of laser transmission welded polyetherketoneketone (PEKK) joints: Influence of process parameters and annealing on weld properties by Marcela Matus-Aguirre, Benoît Cosson, Christian Garnier, Fabrice Schmidt, André Chateau Akué-Asséko, France Chabert

“…The depths of the molten layer (ML) and semi-crystalline layer (scL) vary with laser power and assembly type. …”
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Knot-TPP: A Unified Deep Learning Model for Process Incidence and Tool Wear Monitoring in Stacked Drilling by Jiduo Zhang, Robert Heinemann, Otto Jan Bakker

“…This study proposed a knot–Temporal Pyramid Pooling (TPP) model aimed at monitoring both process incidences and tool wear in the drilling of hybrid stacks, which subsequently informs the machine tool to adjust cutting parameters or, if necessary, replaces the tool. …”
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Influence of the design concept of an experimental demonstration model of a rocket design element on the process of heat and mass transfer by V. I. Trushlyakov, Yu. V. Surikova, D. Yu. Davydovich

“…A conceptual design of an experimental demonstration model of three-layer structure of a launch vehicle body is proposed, based on the replacement of aluminum honeycomb filler with an energy-related material, in particular, based on composite solid rocket propellants. …”
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ATT-BLKAN: A Hybrid Deep Learning Model Combining Attention is Used to Enhance Business Process Prediction by Junyi Xu, Xianwen Fang

“…This is achieved by enhancing the BiLSTM model&#x2019;s ability to capture long-term dependencies through the addition of Agent Attention, while utilising KAN in place of the traditional Multi-Layer Perceptron (MLP) to improve prediction performance and mechanism interpretability. …”
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Load transfer behaviour of super long piles in multi-layer soft soil through field testing and numerical 3D FEM modelling by Thoi Huu Tra, Thanh T. Nguyen, Thien Q. Huynh, Tatsuya Ishikawa

“…The results are then used to not only explore load-transfer process, but also validate numerical modelling through a comprehensive process combining multiple-soil layers and −loading stages. …”
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Improving brain tumor classification: An approach integrating pre-trained CNN models and machine learning algorithms by Mohamed R. Shoaib, Jun Zhao, Heba M. Emara, Ahmed S. Mubarak, Osama A. Omer, Fathi E. Abd El-Samie, Hamada Esmaiel

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Steganographic model to conceal the secret data in audio files utilizing a fourfold paradigm: Interpolation, multi-layering, optimized sample space, and smoothing by Daffa Tristan Firdaus, Ntivuguruzwa Jean De La Croix, Tohari Ahmad, Didacienne Mukanyiligira, Louis Sibomana

“…To address these limitations, this study offers valuable insights to guide researchers in developing high-performing audio steganography models. The proposed method seeks to improve stego audio quality by implementing a smoothing-based technique and optimizing the sample space through linear interpolation, followed by a multi-layering process. …”
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Oxidative Dissolution of Sulfide Minerals in Porous Media Under Evaporative Conditions: Multiphase Experiments and Process‐Based Modeling by Navid Ahmadi, Muhammad Muniruzzaman, Jacopo Cogorno, Massimo Rolle

“…We investigate the oxidative dissolution of pyrite under evaporative conditions and advance a mechanistic understanding of the interactions between multiple physical processes and mineral/surface reactions. We performed a set of experiments in which initially water saturated and anoxic soil columns, containing a top layer of pyrite, are exposed to the atmosphere under no evaporation (single‐phase) and natural evaporative (two‐phase) conditions. …”
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Establishment of a Numerical Model and Process Optimization for the Moving Induction Hardening of a Whole-Roll Flatness Roll by Huaxin Yu, Shuang Liao, Zhichao Li, Ziwei Xu, Shan Li

“…The calculated hardness value of the roll surface in the simulation model reaches 950 HV, which agrees well with the experimental hardness results, validating the ability of the numerical model to guide specific processes.…”
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Simulation of Active Layer Thickness Based on Multi-Source Remote Sensing Data and Integrated Machine Learning Models: A Case Study of the Qinghai-Tibet Plateau by Guoyu Wang, Shuting Niu, Dezhao Yan, Sihai Liang, Yanan Su, Wei Wang, Tao Yin, Xingliang Sun, Li Wan

“…Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. …”
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Aerosols in the Mixed Layer and Mid-Troposphere from Long-Term Data of the Italian Automated Lidar-Ceilometer Network (ALICENET) and Comparison with the ERA5 and CAMS Models by Annachiara Bellini, Henri Diémoz, Gian Paolo Gobbi, Luca Di Liberto, Alessandro Bracci, Francesca Barnaba

“…Through original ALICENET algorithms, we also derive long-term aerosol vertical layering data, including the mixed aerosol layer (MAL) and elevated aerosol layers (EALs) heights. …”
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SIMULATING MACHINING OF CONTINUOUSLY VARIABLE TRANSMISSION PULLEYS IN COMPLEX BLADE TOOL TRAJECTORY IN ANSYS SOFTWARE PACKAGE by A.A. Generalova, A.A. Nikulin, D.S. Bychkov

“…The purpose of the study is to develop a computer model for complex spatial movement of a cutting tool, which makes it possible to study the stress-strain and thermal state of the cutting process, chip formation conditions, predict the quality parameters of the surface layer, and also take into account the characteristics of the workpiece and the cutting tool, with the subsequent possibility of parameterizing the process. …”