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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Research on a PCA-transformer-based prediction algorithm for gas concentration in working face by YANG Jian, SHU Longyong, ZHANG Shulin, QIN Kai, CUI Cong

“…To address this issue, this study proposes a Principal Component Analysis (PCA)-Transformer-based prediction algorithm for gas concentration in working faces. …”
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Evolution from the physical process-based approaches to machine learning approaches to predicting urban floods: a literature review by Md Shike Bin Mazid Anik, Chunjiang An, S. Samuel Li

“…This paper reviews how flood prediction has improved over the last two decades. It begins by reviewing physical process-based models (PPBMs), which often could not handle the fast changes in cities. …”
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Prediction of Chlorophyll-a Concentration Distribution Using Generative Adversarial Network (GAN) Algorithm on Processed Remote Sensing Images by Wattaqwa Amira Zul’Ilmi, Jaya Indra, Iqbal Muhammad

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Advancing Soil Organic Carbon Prediction: A Comprehensive Review of Technologies, AI, Process‐Based and Hybrid Modelling Approaches by Zijuan Ding, Ke Liu, Sabine Grunwald Ph.D., Pete Smith, Philippe Ciais, Bin Wang, Alexandre M.J.‐C. Wadoux, Carla Ferreira, Senani Karunaratne, Narasinha Shurpali, Xiaogang Yin, Dale Roberts, Oli Madgett, Sam Duncan, Meixue Zhou, Zhangyong Liu, Matthew Tom Harrison

“…Integrating data from RS, PSS, and other sensors usually leads to good SOC predictions, provided it is supported by careful calibration, validation across diverse pedo‐climatic and land management, and the use of data processing and modelling frameworks. …”
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Prediction Capability of Analytical Hierarchy Process (AHP) in Badland Susceptibility Mapping: The Foglia River Basin (Italy) Case of Study by Margherita Bianchini, Stefano Morelli, Mirko Francioni, Roberta Bonì

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Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures by Yu Qiao, Marius Grad, Aida Nonn

“…Additive manufacturing (AM), particularly laser powder bed fusion (L-PBF), provides unmatched design flexibility for creating intricate steel structures with minimal post-processing. However, adopting L-PBF for high-performance applications is difficult due to the challenge of predicting microstructure evolution. …”
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High-fidelity surrogate modelling for geometric deviation prediction in laser powder bed fusion using in-process monitoring data by Zhengrui Tao, Mirko Sinico, Bey Vrancken, Wim Dewulf

“…Existing multiphysics simulations and trial-and-error experiments are time-intensive and inflexible for process control. This study targets actual-to-nominal errors within dimensional tolerance, proposing a high-fidelity surrogate model to predict deviations using melt pool monitoring data. …”
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Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm. by Xiaoying Yu, Hongsheng Su, Zeyuan Fan, Yu Dong

“…An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. …”
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A Theoretical Model for Predicting Outness in Lesbian, Gay, and Bisexual People: Minority Stressors, Social Support, and Identity Processes by Rusi Jaspal

“…Discrimination and general social support were directly and positively associated with outness and indirectly through the mediation of sexual identity processes. LGB social support was indirectly associated with outness through sexual identity processes. …”
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Predicting the remaining useful life of metro pantograph sliding strips using gamma processes and its implications for maintenance scheduling. by Jie Liu, Chuang Wu

“…This study proposes an adaptive, data-driven framework for predicting the remaining useful life (RUL) of these components, leveraging operational data from Chongqing Metro Line 6. …”
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Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges by Ebenezer Afrifa‐Yamoah, Eric Adua, Emmanuel Peprah‐Yamoah, Enoch O. Anto, Victor Opoku‐Yamoah, Emmanuel Acheampong, Michael J. Macartney, Rashid Hashmi

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Insights into Octopus maya cathepsins from metatranscriptome and genome: structure evolutionary relationships and functional role prediction in digestive processes by Daisy Pineda-Suazo, Francisco Guillén-Chable, Wendy Itzel Escobedo-Hinojosa, Clara E. Galindo-Sánchez, Carlos Rosas

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Predicting the surface contact angle based on real-time temperature and pressure sintering principles in the fused deposition modeling process by Xunqi Liu, Yan Lou, Jianming Hong, Guijian Huang, Mingyu Liu

“…To delve deeper into the surface wettability of FDM prints, we propose the surface contact angle prediction method (SCART, Surface contact angle prediction based on the real-time temperature) in this paper for predicting the contact angle based on the real-time temperature and pressure sintering principles in the fused deposition modeling process. …”
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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning by Md. Hasan Imam Bijoy, Md. Jueal Mia, Md. Mahbubur Rahman, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Tetsuya Shimamura

“…The study further advocates for integrating high-performing models into the Internet of Medical Things and Robotic Process Automation frameworks, enabling real-time monitoring, predictive analytics, and efficient CKD diagnosis. …”
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GLOPS: A Hybrid Approach for Enhanced Scheduling in Cloud Computing Environments via Machine Learning-Based Process Prediction by Syed Abu Ammar Muhammad Zarif, Layla Kader Suma, Md. Sadat Rahman, Md. Motaharul Islam, Phuoc Hung Pham, Phong Dung Thi Nguyen, Nguyen Thanh Binh

Subjects: “…Process prediction…”
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Predicting Endpoint Temperature of Molten Steel in VD Furnace Refining Process Using Metallurgical Mechanism and Bayesian Optimization XGBoost by Ji XU, Zicheng XIN, Mo LAN, Wenhui LIN, Bo ZHANG, Qing LIU

“…The MM–BO–XGBoost model demonstrates excellent training efficiency and prediction performance.Conclusion The analysis of the mechanism of the VD furnace refining process and Pearson correlation analysis are conducted, and the input variables for the prediction model of VD furnace endpoint liquid steel temperature are identified. …”
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Optimal Cut-Off Value of the Superior Articular Process Area as a Morphological Parameter to Predict Lumbar Foraminal Stenosis by Tae-Ha Lim, Soo Il Choi, Hyung Rae Cho, Keum Nae Kang, Chang Joon Rhyu, Eun Young Chae, Young Su Lim, Yongsoo Lee, Young Uk Kim

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Physical Model for the Simulation of an Air Handling Unit Employed in an Automotive Production Process: Calibration Procedure and Potential Energy Saving by Luca Viscito, Francesco Pelella, Andrea Rega, Federico Magnea, Gerardo Maria Mauro, Alessandro Zanella, Alfonso William Mauro, Nicola Bianco

Subjects: “…model predictive control…”
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Predictive algorithms and criminal justice: expectations, challenges and a particular view of the Spanish VioGén system by Pilar Martín Ríos

Subjects: “…criminal process…”
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