Enhancing Performance and Quality of Transmission Through Knowledge-Driven Machine Learning-Based FWM Mitigation by Sudha Sakthivel, Muhammad Mansoor Alam, Aznida Abu Bakar Sajak, Mazliham Mohd Su'ud, Mohammad Riyaz Belgaum

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Digital twin and contact analysis of ultra-long distance coiled tubing operation structures [version 2; peer review: 1 approved, 2 approved with reservations] by Wenlan Wei, Jiarui Cheng, Hao Qu, Maliang Wang, Wenyuan Wang

“…The reason is that the complex wellbore trajectory makes it difficult to evaluate the contact state between coiled tubing and wellbore, resulting in a significant error in the pipe string mechanics calculation and analysis. …”
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A population Monte Carlo model for underwater acoustic telemetry positioning in reflective environments by James Adam Campbell, Samuel Joseph Shry, Petter Lundberg, Olle Calles, Franz Hölker

“…A novel time‐of‐arrival model for telemetry positioning is presented that utilizes a population Monte Carlo algorithm to solve positions (termed PMC‐TOA). Telemetry detection error is modelled as a mixture distribution, allowing reflected detections to be identified and positions to be estimated despite their presence. …”
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Study on strength prediction of grouting filling body in karst areas considering pressure filter effect by Zhao Yan, Zhao Yan, Duan Jihong, Liu Yinglin, Liu Yinglin, Xie Mengshan, Xie Mengshan, Li Wen

“…Given the increasing number of similar cases, the research findings hold significant application potential and practical value.…”
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Optimized design and performance evaluation of a highly precise variable rate mis-planting and replanting potato electronic-metering mechanism by Abdallah Elshawadfy Elwakeel, Ahmed Elbeltagi, Ali Salem, Ali Salem, Ahmed Z. Dewidar, Ahmed Z. Dewidar

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A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study by Guilherme Cassales, Serajis Salekin, Nick Lim, Dean Meason, Albert Bifet, Bernhard Pfahringer, Eibe Frank

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Continuous theta-burst stimulation of the prefrontal cortex in the macaque monkey: no behavioral evidence for within-target inhibition or neural evidence for cross-hemisphere disin... by Sebastian J. Lehmann, Brian D. Corneil

“…Although widely used in basic research and clinical settings, there lacks an understanding of the neurophysiological and behavioral effects of cTBS. …”
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Modular Neural Network Model for Biometric Authentication of Personnel in Critical Infrastructure Facilities Based on Facial Images by Oleksandr Korchenko, Ihor Tereikovskyi, Ruslana Ziubina, Liudmyla Tereikovska, Oleksandr Korystin, Oleh Tereikovskyi, Volodymyr Karpinskyi

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Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks by Zurisaddai Severiche-Maury, Carlos Eduardo Uc-Rios, Wilson Arrubla-Hoyos, Dora Cama-Pinto, Juan Antonio Holgado-Terriza, Miguel Damas-Hermoso, Alejandro Cama-Pinto

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Time frequency analysis of elastic wave PSO OMP for defects in flat steel of down conductors by Jing Zhang, Wei Liu, Minghui Bao, Peng Yang, Xuhua Liu, Zhihong Fu

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Improving large-scale snow albedo modeling using a climatology of light-absorbing particle deposition by M. Gaillard, M. Gaillard, V. Vionnet, M. Lafaysse, M. Dumont, P. Ginoux

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Analytical Solution for Longitudinal Response of Tunnel Structures Under Strike-Slip Fault Dislocation Considering Tangential Soil–Tunnel Contact Effect and Fault Width by Helin Zhao, Qingzi Wu, Yao Zeng, Liangkun Zhou, Yumin Wen

“…Ignoring this effect leads to an error of up to 35.33% in the peak value of the structural bending moment. …”
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Improving the Flexibility of Coal-Fired Power Units by Dynamic Cold-End Optimization by Yanpeng Zhang, Xinzhen Fang, Zihan Kong, Zijiang Yang, Jinxu Lao, Wei Zheng, Lingkai Zhu, Jiwei Song

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Novel Mathematical Modeling Parameters of PEMFC Based on Newton–Raphson Iterative Method by Ahmed A. Zaki Diab, Ahmed Hamdy Ali, Hamdy M. Sultan, Mohamed A. Ismeil, Montaser Abdelsattar

“…The suggested model attained a relative error of under 0.5% and SSE values as low as <inline-formula> <tex-math notation="LaTeX">$2.9795\times 10^{-7}$ </tex-math></inline-formula> for five measurement locations in the SR-250W stack, hence affirming the estimation&#x2019;s excellent precision.…”
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Neural network backstepping control of OWC wave energy system by Priyanka Nath, Sunil Kumar Mishra, Amitkumar Vidyakant Jha, Bhargav Appasani, Akshaya Kumar Pati, Vijay Kumar Verma, Philibert Nsengiyumva, Avireni Srinivasulu

“…To address these challenges, this research paper introduces a Chebyshev NN within the BSC framework, leveraging its high approximation accuracy and computational efficiency. …”
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An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection by Farouq Zitouni, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed, Saad Harous

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Evaluation and analysis of the precipitable water vapor in Inner Mongolia of China by Qi Bai, Qiaoli Kong, Xiaolong Mi, Wu Chen, Junsheng Ding, Yunqing Huang, Meiqi Li, Qian Li

“…The comparison between GNSS PWV and RS PWV reveals an average bias of −0.68 mm and a root mean square error (RMSE) of 2.17 mm, indicating the high accuracy of GNSS PWV and its potential as an assessment tool of other PWV products. …”
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A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar by Saurabh Kumar, Uruya Weesakul, Divesh Ranjan Kumar, Pradeep Thangavel, Warit Wipulanusat, Jirapon Sunkpho

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Deep learning meets tree phenology modelling: PhenoFormer versus process‐based models by Vivien Sainte Fare Garnot, Lynsay Spafford, Jelle Lever, Christian Sigg, Barbara Pietragalla, Yann Vitasse, Arthur Gessler, Jan Dirk Wegner

“…When predicting under climatic conditions similar to the training data, our model improved over the best process‐based models by 6% normalised root‐mean‐squared error (nRMSE) for spring phenology and 7% nRMSE for autumn phenology. …”
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Wind speed and direction on predicting wind energy availability for a sustainable ecosystem by B. Baranitharan, D. Sivakumar, M. Perarul Selvan

“…The study discovered that the three-parameter Weibull distribution caused less error in the wind speed data distribution pattern than both the Weibull distribution and beta distribution, based on the results of the Anderson-Darling and Kolmogorov-Smirnov tests. …”
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